Predictors of medical and non-medical motives of cannabis use in Finland: A cross-sectional survey study
Tampere University, Faculty of Social Sciences, Emerging Technologies Lab, 33014 Tampere, Finland
University of Virginia, School of Education and Human Development, Charlottesville, VA 22904, USA
Finnish Institute for Health and Welfare (THL), 00271 Helsinki, Finland
Åbo Akademi University, Department of Psychology, 20500 Turku, Finland
∗Corresponding author aleksi.hupli@tuni.fiSummary
Policies regarding cannabis continue to develop worldwide as scientific research into benefits and harms for both therapeutic and recreational use becomes more detailed. Here, we report results from a cross-sectional survey (n = 537) from Finland which aimed to identify factors associated with cannabis use for recreational versus medical purposes. Several demographic and cannabis related factors were used to predict medical cannabis use with logistic regression analysis. Medical cannabis use was associated with female gender, older age, lower education, using edible cannabis products such as oils, and using cannabis more frequently compared to recreational use. The study points toward important demographic, behavioral, and use factors differentiating recreational and therapeutic use of cannabis. These are relevant to consider when designing future studies as well as appropriate harm reduction and cannabis policy measures for the needs of different people who use cannabis and cannabinoids for multifactorial motives experiencing various effects.
Graphical abstract
Highlights
- •We explored factors differentiating recreational and therapeutic use of cannabis
- •Medical users are likely less-educated older females who use edible cannabis
- •There are also notable similarities between medical and recreational use in Finland
- •These are important to consider when designing future studies and cannabis policies
Teaser
Health sciences; Medicine; Medical substance; Substance of abuse; Social sciences; Psychology; Sociology
Article notes
Published: April 22, 2025
Introduction
The plant Cannabis sativa L. was consumed worldwide by an estimated 228 million people at least once in 2022.1 As an illegalized drug or legalized medicine, cannabis in its many forms is often used with medical intention, even when the cannabis product used is not medical grade or prescribed by a clinician. Such self-medicative use is illegal in most national jurisdictions. Even if there is technically legal access to cannabinoid therapy, like in Finland, patient access and clinical practice is often challenging. Cannabinoid therapy in this context refers to the use of cannabinoid-based medical products (CBMPs) in the treatment of official disease diagnosis,2 which has been possible in Finland in its modern form since 2008.3
Despite early legal access to CBMPs in Finland there are no national treatment recommendations or research programs to monitor medical cannabis use. To our knowledge there are only a couple of case studies of patients with official CBMP prescriptions; one with a primary diagnosis of adult ADHD4 and one with cerebral palsy.5 Contrary to global trends, the number of medical cannabis prescriptions have decreased in recent years.6,7 In Finland, a total of 1,218 medical cannabis prescriptions were issued between 2017 and 2021 with the same patients likely having multiple prescriptions.6 At the same time period in Australia, for example, more than 130,000 medicinal cannabis prescriptions were issued mainly by general practitioners.8 About 65% of patients in Australia who were prescribed medical cannabis have chronic (non-cancer) pain and several other indications have also been monitored.8 On the other hand, in an article published in a Finnish medical journal in 2016, the authors state that “there is very little evidence to support the clinical use of cannabinoids in any condition except spasticity related to multiple sclerosis”.6,9 This is a markedly different conclusion compared to the above-mentioned clinical practice in Australia, and a report by National Academy of Science, Engineering, and Medicine10 in the USA. The report states that in addition to improving self-reported spasticity symptoms of MS patients, there is evidence that especially oral cannabinoids are effective antiemetics for chemotherapy induced nausea and as analgesics for adults with chronic pain.10,11
Thus, there is controversy regarding medical efficacy of cannabis and cannabinoids which has an impact on national prescribing practices and patient access, among other things. Given the practical difficulties of attaining prescribed medical cannabis in Finland, several patients use illegally sourced cannabis for medical purposes, and the number of people using non-medical grade cannabis products for self-medication is estimated to be significantly larger compared to official prescription holders. Based on data from the 2014 national survey it was estimated that there were between 2,000–5,000 Finns who use non-medical grade cannabis for medical purposes.12 The lifetime prevalence of cannabis use among 15–69-year-olds in Finland has continued to increase from 19% in 2014 to 25.6% in 2018 and to 31.2% in 2022.13 Thus, the number of people using cannabis for self-medication has likely increased as well, requiring improved understanding of this growing and hard-to-reach population.
Attitudes toward cannabis in Finland have changed as well with 24% of the population in 2022 saying cannabis should be accessed legally for any reason, compared to 10% in 2010, and 56% saying it should be legally accessible for medical reasons, compared to 40% in 2010.13 Despite patients in Finland reporting beneficial effects from using cannabis, there is still hardly any research on Finnish medical cannabis patients.4,5,7,14 Non-clinical studies conducted in Finland have shown pain patients who use cannabinoids reporting similar reduction of pain intensity compared to patients using opioids.14 However, patients using cannabinoids also reported more positive factors compared to medical opioid users in relation to emotions, functionality and overall well-being with no differences in reported side effects.14 Older age, being female, earlier onset age of cannabis use and living in smaller city were found to be predictors of a medical motive to use cannabis among a sample of younger users who reported both desired and undesired effects from their recreational and medical cannabis use.15
Medical motives to use cannabis have also been reported among Finnish people who grow their own cannabis,16,17 possibly partly due to the difficulty in accessing medical grade products under medical supervision. Compared to Danish cannabis growers, a significantly higher portion of Finnish growers reported medical motives as their main motivation to grow (58.8% in Finland compared to 23.7% in Denmark).18 In a six-country comparison among cannabis growers,16 the main medical motivation for Finnish growers was to use cannabis for depression and other mood disorders (40.4%), followed by chronic pain (27.8%) and anxiety or panic disorders (26.8%).
Sixty-nine per cent of the Finnish growers reported having an official diagnosis, but the majority (67.5%) had not been recommended or discussed the use of cannabis with their doctor.16 The proportion of people not discussing their cannabis use with their doctors was the highest in Finland compared to the other countries and the authors of the study consider the reason for this “practice of silence” to be the strict Finnish drug policies.16 These criminalization policies have been under pressure for reform in recent years.19 For instance, two Citizens Initiatives, one on cannabis decriminalization in 2019,19 and one on cannabis legalization in 2023, managed to gather required 50 000 signatures to be processed and debated by the Finnish parliament. Both initiatives made arguments regarding improved access for medical users. While the decriminalization initiative was eventually rejected by the Parliament in 2022, the legalization initiative is still being processed. In 2021, the Finnish Green Party also became the first major party to include cannabis legalization in their official party program, which also created a debate in social media.20
How Finnish policies on cannabis develop in upcoming years remains to be seen, but Nordic drug policies in general are said to be in the crossroads and affected by reforms across Europe21 and the rest of the world. EU countries such as Germany, the Netherlands, the Czech Republic, Malta and Luxembourg have changed or announced plans to change their legislation regarding the use of non-medical cannabis22 which potentially can have an impact in Finland as well. Especially Germanýs new cannabis act in 2024 is argued to signify “the dawn of a new era for cannabis policy in Europe”23 even though the extent of changes of the new law to public health and other relevant factors remains to be seen. Rescheduling of cannabis and its constituents in the United Nations drug treaties in 2020 based on a critical review and recommendations by the World Health Organization24,25 and potential rescheduling on a federal level in the USA could also have an impact in Finland, at least in the form of increased scientific research on medical properties of cannabis and cannabinoids.
Therefore, it is necessary to gain a better understanding of individuals who currently use cannabis in order to track potential changes in cannabis use. Finland already has the highest prevalence of non-medical cannabis use in the past year specifically among young people aged 15–34 compared to other Nordic countries.26 Using AI methodology to predict various risk factors for non-medical cannabis use in Finland, Unlu et al.27 found that social factors like being offered cannabis for free, or to be purchased and having a friend who uses illicit drugs were the most predominant factors predicting cannabis use based on national survey data. A recent survey among Finnish vocational students (n = 1855) also showed that about 20% (n = 375) of study participants had used cannabis in the past six months. Of those who had used cannabis, 20% (n = 77) experienced problems related to use, mainly in relation to memory, concentration and time management.28
In addition to various risk factors and pharmacological effects, people who use cannabis and other drugs in Finland also experience various politicogenic drug effects, i.e., effects stemming from drug policing.29 Stigma, fear of “getting caught” and different social and legal control measures are examples of negative politicogenic drug effects experienced by cannabis users and activists in the Finnish context.30,31,32 Despite consequential politicogenic drug effects, the prevalence of cannabis use continues to grow15 and is partly integrating into the mainstream.32 The increasing popularity of cannabis use in Finland is understandable in the context of reported desired effects, both in relation to recreational and medical use.14,32
Survey studies in general looking at differences between medical and recreational use often have different methodologies, samples and questionnaires which makes comparing findings challenging and sometimes contradictory. The line between medical and recreational use is also often blurry as a portion of people who use cannabis in countries like Canada,33,34 USA,35 Denmark,36 Sweden,37 and Finland12,38 report having experiences of both recreational and medical cannabis use and/or their medical use happens without an official medical prescription. The observed differences between medical and recreational cannabis use are impacted by factors like frequency of use, route of administration, dose, health and well-being, age and indeed different motives for cannabis use.39 However, research comparing medical and recreational users are not consistent in relation to these factors. For instance, a survey study in the UK found medically motivated users to be older but did not find gender differences40 while an interview study among primary care visitors in the USA41 did not find age differences between recreational and medical cannabis users, while gender difference was statistically significant with women more likely using cannabis medically than recreationally (p < 0.05). While these findings can also reflect differences in sampling and methodology, taking the country-context into account is also an important factor to consider.
A study across 12 countries, including Finland, among participants of the Global Cannabis Cultivation Research Consortium (GCCRC) found group differences among cannabis growers who grow cannabis for recreational purposes compared to medical cannabis growers with and without experiences with other illicit drug use.17 Some of these differences involved sociodemographic differences like age and gender, medical growers being more often female and older compared to recreational growers. Other differences involved user practices like frequency of cannabis use, medical growers using cannabis more often than recreational users, and other drug use, medical growers using less alcohol and tobacco in comparison. Perhaps unexpectedly, motivations for growing cannabis also differed, medical growers reporting more health motivations compared to recreational growers and medical growers without other illicit drug use were also less involved in criminal activity compared to the other groups.17
Another more recent study by the GCCRC across 18 countries explored whether severity of dependence (Severity of Dependence Scale; SDS) differed between cannabis growers with recreational only motive (mean SDS score 5.54) compared to medical only motive (mean SDS: 4.75) and a third group with both medical and recreational motive (mean SDS: 5.19).42 Similar to earlier findings,17 medical cannabis users were also found to be older, more likely to be female and reporting more frequent use of cannabis and less use of other illicit substances.42 Medically motivated users also reported using a wider variety of types of cannabis like oils, extracts and edibles which was also associated with lower SDS scores. The authors of the study call for more research to better understand the differences and similarities of people who use cannabis for recreational and therapeutic purposes.42 There is also a need to know more about these differences and similarities in specific country contexts, like Finland.
In this study, we report results from a cross-sectional online survey (n = 537) which was targeted for people living in Finland with experience of using non-medical grade cannabis for self-medication. Our primary research question was: what sociodemographic and cannabis use factors are associated with medical versus recreational use of cannabis? Our focus was on people who report as their main motivation to use cannabis as either mainly medical or mainly recreational motive.
Method
Procedure
The online survey was aimed at Finnish people over the age of 18 and who had experience with at least occasionally using cannabis flower, oil or hash for self-medicative reasons without an official medical prescription. The survey was open from early April to mid-June in 2024 and the link to the survey was shared mainly on social media (Facebook, Instagram, and X, formerly Twitter). Flyers calling for participants were also physically distributed in specialized shops in the capital area of Helsinki that sell legal cannabis-related products (e.g., seeds, bongs, and other smoking related paraphernalia or non-THC cannabinoid products). The survey was also shared by non-governmental organizations (NGOs) like the Finnish Cannabis Association, the Finnish Association for Humane Drug Policy, and The Network for Preventive Substance Abuse Work. The Network consists of over 60 social and health care organizations and the survey was shared in their internal newsletter (on April 12, 2024) which is targeted mainly for professionals working in the field; the aim was to reach the study subjects through the professionals.
Prior to the launching the survey the study protocol was approved by Tampere University Data Protection Office (on March 4, 2024) based on the provided Data Protection Impact Assessment (DPIA) and by the Ethics Committee of the Tampere Region (decision number 20/2024) which oversees the ethical reviews of proposed non-medical research in the field of human sciences at Tampere University. The survey was anonymous, and the participants were asked to provide their informed consent and that they are over the age of 18 before participating in the survey. The survey was developed and operated on LimeSurvey provided by Tampere University.
Measures
Cannabis use motivation was initially measured by four categories, adapted from the Global Drug Survey.
- (1)Primarily for self-medication (n = 79)
- (2)Mostly for self-medication, with occasional recreational use (n = 301)
- (3)Mostly for recreational use, with occasional self-medication (n = 136)
- (4)Primarily for recreational purposes (n = 21)
For the analysis, we recoded these categories into a binary variable, classifying cannabis use as either mainly medical or mainly recreational. Participants who reported using cannabis mostly or primarily for recreational purposes were grouped into the mainly recreational category (n = 157, 29%, reference category), while those using it mostly or primarily for medical purposes were classified in the mainly medical category (n = 370, 71%). For analytical purposes in the results section of the article we will refer to these groups as recreational and medical even though majority of respondents reported both motives.
The survey contained measures about demographic background, including age, gender (male, female, other, not willing to disclose), education (Elementary school or less, Vocational Upper secondary, Regular Upper secondary, and Higher Education which included Bachelor, Master, or Higher degrees), population size of the participant’s place of residence and employment status (Full-time, Part-time, Student, Retired, Unemployed, Disabled/Sick). Additionally, we probed about practices of cannabis use, including onset age of initiating cannabis use (at what age did you use cannabis for the first time), preferred way of use (smoking with tobacco, smoking pure, vaporizing, eating or drinking, and other ways of use), amount used per use session (Less than 0.1 g; 0.1–0.5 g; 0.6–1.0 g; 1.1–1.5 g; 1.6–2.0 g; 2.1–2.5 g; ” 2.6–3.0 g; and more than 3 g), frequency of use (days in the previous month), and self-estimated THC and CBD concentrations (“low”, “moderate”, “high”, “I do not know”).
Following Sznitman et al.42 we also used the first four items of the Severity of Dependence Scale (SDS) which focus on psychological aspects of dependence. The first four items of the scale are: Do you think your use of cannabis is out of control?, Does the prospect of missing a dose of cannabis make you worried?, Do you worry about your use of cannabis? and Do you wish you could stop the use of cannabis? The response options range from “never/almost never”, “sometimes”, “often”, and “always/almost always”. Similar to Sznitman et al. we also included response options “I do not know”, “I have not used in the past 3 months”, and “I do not want to answer”. According to Sznitman et al.42 the fifth item on the original validated scale which measures difficulty of being without cannabis is not a good fit when comparing medical and recreational groups and it was thus left out.
Additionally, the survey included more specific questions related to medical use based on a research protocol developed in the MEDUSA project (MEDicinal Use of cAnnabis: Motives, patterns of use and barriers to treatment) by the Dutch Trimbos Institute.43 These questions were, for example, about primary health indications and symptoms, symptom relief effectiveness, quality of life changes, effects of cannabis on other medications and the Cannabis Effects Expectancy Questionnaire–Medical (CEEQ-M)44 but these will be reported elsewhere.
Analytical approach
The sample was divided into two groups based on primary cannabis use motivation. Pairwise differences in demographic and use-related variables between the groups were examined with chi-square tests for categorical variables, and t-tests for continuous or ordinal variables. Next, logistic regression was used to examine the association between group membership (mainly recreational use coded as 0, mainly medical use coded as 1) and the demographic and use-related factors. Unlike pairwise comparisons, which assess variables independently, logistic regression considers all independent variables simultaneously. This approach accounts for potential covariances or interdependencies among the predictors, providing estimates of each variable’s unique contribution to predicting cannabis use motivation while statistically controlling for the influence of the other variables in the model.
To ensure consistency and comparability with prior research, in logistic regression we began by constructing a base model using a set of seven predictor variables that were previously evaluated in a similar analysis on a different dataset,15 namely gender, age, education, age of onset, city size, monthly cannabis use frequency, and amount of cannabis used per session. The extended model added five additional variables: employment status, type of cannabis usage, self-estimated THC and CBD concentration, and severity of dependence. By comparing the base model with the extended model, we aimed to determine whether these additional variables contributed significantly to the overall model fit and whether it offered a more comprehensive understanding of the factors associated with the motivation behind cannabis use. For the models, we report odd ratios (OR), their 95% confidence intervals and p-values. To estimate the model fit, we report the Hosmer-Lemeshow goodness-of-fit test, Tjur’s R2, the likelihood ratio test (ANOVA), Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC). All the analyses were performed using the R (4.4.1) statistical software.
Results
Differences between the groups: Pairwise comparisons
We examined the group differences in demographic and use-related factors using pairwise comparisons, presented in Tables 1 and 2. The sample included 348 males (64.8%), 153 females (28.5%), 22 individuals who identified as other gender (4.1%), and 14 who chose not to disclose their gender (2.6%). Males represented the majority among both recreational (68.8%) and medical (63.2%) cannabis users, while females constituted 22.9% of recreational users and 30.8% of medical users. The proportion of individuals identifying as another gender or not responding was similar across the groups. The chi-square test indicated no significant difference in gender distribution between recreational and medical cannabis users (p = 0.253). Education level was significantly associated with the type of cannabis use (p = 0.040). In both the medical and recreational group, the most frequent education level was vocational degree (44.5% and 37.8%, respectively), however, higher education was generally more frequent in the recreational group. Participants were categorized by city size, with the largest proportion residing in the capital area (31.7%). Among recreational users, 38.2% lived in the capital area, compared to 29.0% of medical users. The chi-square test indicated no statistically significant association between city size and the motivation of cannabis use (p = 0.218), suggesting that urban or rural residency is not substantially associated with cannabis use motivation in this sample (Table 1).Independent variables Mainly recreational use n (%) Mainly medical use n (%) Total n (%) p-value Gender Male 108 (68.8) 240 (63.2) 348 (64.8) 0.253 Female 36 (22.9) 117 (30.8) 153 (28.5) Other 8 (5.1) 14 (3.7) 22 (4.1) Not responding 5 (3.2) 9 (2.4) 14 (2.6) Education Elementary school or less 18 (11.5) 67 (17.6) 85 (15.8) 0.040 Vocational degree 59 (37.8) 169 (44.5) 228 (42.5) Upper secondary degree 29 (18.5) 50 (13.2) 79 (14.7) Bachelor’s degree 28 (17.9) 61 (16.0) 89 (16.6) Master’s degree or above 23 (14.7) 33 (8.7) 56 (10.4) City size Less than 50,000 39 (24.9) 109 (28.7) 148 (27.6) 0.218 50,000–100,000 23 (14.7) 62 (16.3) 85 (15.8) More than 100,000 35 (22.3) 99 (26.0) 134 (25.0) Helsinki metropolitan area (>1,000,000) 60 (38.2) 110 (29.0) 170 (31.7) Employment Employed 112 (71.3) 216 (56.8) 328 (61.1) 0.003 Not in paid employment 45 (28.7) 156 (41.0) 201 (37.4) Other 0 (0) 8 (2.1) 8 (1.5) Usage type Eat or drink 6 (3.8) 62 (16.3) 68 (12.6) <0.001 Smoke pure 33 (21.0) 62 (16.3) 95 (17.7) Smoke with tobacco 68 (43.3) 141 (37.1) 209 (39.0) Vaporize flower 50 (31.9) 108 (28.4) 158 (24.4) Other 0 (0) 7 (1.8) 7 (1.3) THC concentration High 13 (8.2) 66 (17.4) 79 (14.7) 0.004 Moderate 46 (29.3) 133 (35.0) 179 (33.3) Low 43 (27.4) 93 (24.5) 136 (25.3) I do not know 55 (35.3) 88 (23.2) 143 (26.6) CBD concentration High 51 (32.5) 116 (30.5) 167 (31.1) 0.132 Moderate 63 (40.1) 166 (43.7) 229 (42.6) Low 7 (4.5) 34 (9.0) 41 (7.6) I do not know 36 (22.9) 64 (16.8) 100 (18.6) Amount Less than 0.1 g 8 (5.1) 29 (7.6) 37 (7) 0.168 0.1–0.5 g 84 (53.5) 163 (42.9) 247 (46) 0.6–1.0 g 36 (22.9) 96 (25.3) 132 (24.6) 1.1–2.0 g 22 (14.0) 61 (16.0) 83 (15.4) 2.1 g or more 7 (4.5) 31 (5.8) 38 (7.1) Independent variables Mean Min Max p-value Age Recreational users 35.2 18 63 0.002 Medical users 38.5 18 73 Onset age (years) Recreational users 18.5 12 45 0.301 Medical users 19.2 9 66 Monthly usage (number of days) Recreational users 15.3 0 30 <0.001 Medical users 19.4 0 30 Severity of dependence Recreational users 1.94 0 10 0.170 Medical users 1.69 0 11
To create a simplified employment variable, we collapsed some categories in the original questionnaires into three broader groups: “Employed”, “Not in paid employment”, and “Other”. Specifically, “Full-time”, “Part-time”, and “Student” were grouped as “Employed”, while “Unemployed”, “Disabled/Sick”, and “Retired” were classified as “Not in paid employment”. This approach allowed us to capture essential employment distinctions in a simplified structure, enhancing interpretability of the results. Employment status was significantly associated with cannabis use motivation (p = 0.003). Among recreational users, 71.3% were employed while 28.7% were not in paid employment. In contrast, a larger proportion of medical users (41%) were not in paid employment, compared to 56.8% who were employed. These results indicate that medical users are less likely to be employed (Table 1).
A significant association was observed between the type of cannabis administration (e.g., smoking, vaporizing, eating) and the motivation of cannabis use (p < 0.001). Both groups predominantly smoked cannabis with tobacco (37.1% in the medical group and 43.3% in the recreational group) but eating or drinking cannabis was more frequent in the medical group (16.3%) than in the recreational group (3.8%, Table 1). Self-estimated THC concentration differed significantly between recreational and medical users (p = 0.004). Overall, the estimated amount of THC was higher in the medical group, and not knowing the THC content was more frequent in the recreational group. More specifically, 17.4% of medical users reported using high-THC products, compared to only 8.2% of recreational users. Recreational users reported a higher rate of uncertainty about THC levels, with 35.3% indicating “I do not know” (vs. 23.2% of medical users).
There was no significant difference in self-estimated CBD concentrations between recreational and medical cannabis users (p = 0.132). The majority of users, regardless of motivation, reported using products with moderate CBD concentration (total 42.6%). High CBD concentration was reported by 32.5% of recreational users and 30.5% of medical users, while CBD concentrations were estimated to be low by 4.5% of recreational users and 9% of medical users. The amount of cannabis used per session did not significantly differ between recreational and medical users (p = 0.168). The most reported amount across both groups was between 0.1 and 0.5 g, with 53.5% of recreational users and 42.9% of medical users falling into this category. Other amounts, such as 0.6 to 1.0 g per session, were also relatively common, reported by 22.9% of recreational users and 25.3% of medical users.
Age was significantly associated with the motivation for cannabis use (p = 0.002). Recreational users had an average age of 35.2 years, while medical users were slightly older, with an average age of 38.5 years. The age of onset was not significantly different between recreational and medical users (p = 0.301). Recreational users started using cannabis at an average age of 18.5 years, while medical users began at an average age of 19.2 years (Table 2). Monthly usage frequency was significantly different between the two groups (p < 0.001). Recreational users reported an average of 15.3 days per month, while medical users reported a higher average usage of 19.4 days per month (Table 2).
The severity of cannabis dependency was measured using four questions from the validated Severity of Dependence Scale (SDS).42,45 The self-rated severity of anxiety, feelings of anxiousness, concerns about cannabis use, and desire to stop using cannabis were each initially measured on a 7-point scale: “Never or very rarely”, “Sometimes”, “Often”, “Always or almost always”, “I can’t say”, “I have not used in the last 3 months”, and “I don’t want to answer”. To consolidate these variables into a single composite severity measure, we first recoded the 7-point scale into 4 levels. Specifically, we combined “I can’t say”, “I have not used in the last 3 months”, “I don’t want to answer”, and “Never or very rarely” into a single category coded as 0. The remaining categories were coded as follows: “Sometimes” as 1, “Often” as 2, and “Always or almost always” as 3, in a similar way as Sznitman et al.42 We then aggregated these four variables into a single composite variable, named “Severity”, by summing the scores for each respondent. Lower scores (theoretical minimum 0) indicated lower severity levels, while higher scores (theoretical maximum 12) reflected greater severity of dependence. Cronbach’s alpha was alpha = 0.74, indicating acceptable internal consistency. The severity of cannabis dependency did not significantly differ between recreational and medical users (p = 0.170). Recreational users had a mean severity score of 1.94, while medical users had a mean score of 1.69.
Differences between the groups: Logistic regression analysis
Due to the low number of observations in the gender categories “Other” and “I do not want to answer”, we dummy-coded gender into two categories. Given that males represented the majority, we used males as the reference group (0) and combined the other categories to examine whether this combined group of female and others (1) was associated with the cannabis use motivation. Moreover, some categorical variables were recategorized to reduce their number of levels and to preserve statistical power. In the preferred usage type variable, 7 observations fell into the “Other” category, representing unique cannabis administration methods (e.g., CBD-pouches). Including these in the analysis was not justified due to the low number of observations and combining them with existing categories was not theoretically justified. Therefore, we excluded these observations from the logistic regression analysis. Consequently, the final dataset for the logistic regression model consisted of n = 209 observations for “Smoke with tobacco”, n = 68 for “Eat or drink”, n = 95 for “Smoke pure”, and n = 158 for “Vaporize flower”.
In the employment variable, seven participants did not disclose their employment status. These cases were imputed based on the proportional distribution of the remaining categories in each group. Since we dropped the 7 cases in usage type categories, and following mentioned adjustments, the counts shifted from n = 324 to n = 331 for “Employed” and from n = 198 to n = 199 for “Not in paid employment”.
The Hosmer-Lemeshow goodness-of-fit test yielded p-values of 0.643 for the base model and 0.845 for the extended model, indicating adequate fit and that the model estimates did not significantly differ from the observed values. The likelihood ratio test (ANOVA) indicated a significant improvement in model fit for the extended model over the base model (p < 0.001). The Bayesian information criterion (BIC) supported the base model (BIC = 656.575, df = 8) over the extended model (BIC = 668.852, df = 15), however, the Akaike information criterion (AIC) favored the extended model (AIC = 604.759) over the base model (AIC = 622.392). Overall, the extended model demonstrated improved explanatory power over the base model, with Tjur’s R2 increasing from 0.07 in the base model to 0.12 in the extended model. Given that the same predictors were significant in both the base- and extended model and given that the extended model showed on average better model fit, we henceforth report the results solely for the extended model.
Gender was statistically significant predictor. Those in the gender category female or other had higher odds of using cannabis for medical purposes compared to males, with an odds ratio of 1.63 in the extended model (95% CI: 0.15–3.99, p = 0.038). This suggests that females and others were approximately 63–72% more likely to use cannabis medically than recreationally (Table 3; Figure 1). Since we merged the gender categories, we conducted a sensitivity analysis by excluding respondents who selected other gender categories or did not answer this question, retaining only male (n = 342) and female (n = 152) respondents (total n = 494). The overall model results remained consistent in terms of direction and significance of variables. However, we observed an increase in the odds ratio in both the base model (odds ratio = 1.94, 95% CI: 1.22–3.14, p = 0.006) and the extended model (odds ratio = 1.88, 95% CI: 1.15–3.13, p = 0.013) for females.Estimates Base Model Extended Model OR 95% CIs p-values OR 95% CIs p-values (Intercept) 0.88 0.27–2.78 0.831 0.77 0.15–3.99 0.756 Gendera (ref. Male) 1.72 1.12–2.66 0.014 1.63 1.03–2.61 0.038 Age 1.03 1.01–1.05 0.002 1.03 1.01–1.05 0.016 Education 0.81 0.68–0.95 0.012 0.79 0.66–0.95 0.012 Onset age 1.00 0.97–1.03 0.907 0.98 0.95–1.02 0.29 City size 0.91 0.76–1.08 0.274 0.88 0.73–1.06 0.177 Monthly usage 1.03 1.01–1.05 0.001 1.05 1.02–1.07 <0.001 Amount 0.99 0.80–1.22 0.926 1.08 0.87–1.36 0.482 Employment (ref. Employed) – – – 1.20 0.76–1.92 0.431 Usage type (ref. Smoke with tobacco) Eat or drink – – – 6.34 2.58–18.21 <0.001 Smoke pure – – – 1.29 0.73–2.28 0.383 Vaporize flower – – – 1.39 0.85–2.31 0.197 Severity of dependence – – – 0.92 0.82–1.03 0.134 THC concentration – – – 1.30 0.91–1.85 0.153 CBD concentration – – – 0.85 0.56–1.29 0.439 Tjur’s R2 0.07 0.12
Age was also positively associated with medical cannabis use, with each additional year associated with a slight increase in the likelihood of medical use. The odds ratio for age was 1.03 (extended model 95% CI: 1.00–1.05, p = 0.016), suggesting that older individuals are more likely to use cannabis for medical purposes with one additional year of age increases the odds of being a medical user by approximately 3% (Table 3; Figure 1). Education level was inversely associated with medical cannabis use. Each additional level of education decreased the likelihood of medical use, with an odds ratio 0.79 in the extended model (95% CI: 0.66–0.95, p = 0.012). This suggests that individuals with higher education are less likely to use cannabis for medical purposes and more likely to use it recreationally (Table 3; Figure 1).
Monthly usage frequency in the past month was a significant predictor in both models, with higher usage frequency associated with an increased likelihood of medical cannabis use. The odds ratio for monthly usage was 1.05 in the extended model (95% CI: 1.02–1.07, p < 0.001), indicating that each additional day of use per month increased the likelihood of using cannabis for medical rather than recreational purposes by approximately 3–5% (Table 3; Figure 1). In the extended model, type of cannabis usage was a significant predictor of medical use motive. Eating or drinking cannabis predicted medical use motive compared to smoking cannabis with tobacco (OR = 6.34, p < 0.001), smoking pure cannabis (OR = 4.93, p = 0.006), and vaporizing flower (OR = 4.56, p = 0.006); the other contrasts were not significant (p > 0.197) (Table 3; Figure 1).
To address the potential correlation between education and employment, we conducted additional analyses. A Chi-squared test with Cramér’s V indicated a moderate association between education and employment status (Cramér’s V = 0.29, p < 0.001). Further, a Pearson correlation between education and a binary employment variable (1 = Employed, 0 = Not Employed) revealed a weak to moderate positive relationship (r = 0.264, p < 0.001, 95% CI: 0.183–0.342). These results suggest that while education and employment are related, their correlation is not strong enough to imply that one variable serves as a proxy for the other. This finding supports the conclusion that education is an independent predictor in our model.
Discussion
The use of non-medical grade cannabis for medical purposes is a common phenomenon but knowledge of this patient population is limited. This study aimed to investigate sociodemographic and user practice differences between Finnish participants who use cannabis mainly recreationally versus mainly medically. The pairwise comparisons indicated that mainly medical users are less educated, more likely not employed, more likely to use edible forms of cannabis, and estimate their cannabis products to have higher THC content than mainly recreational users (Table 1). Moreover, medical users were slightly older and used cannabis more often than recreational users (Table 2). The logistic regression analyses mainly corroborated these findings, except for THC content and employment status which became non-significant.
In the base model, female and other gender, older age, lower education level, and higher monthly use frequency predicted mainly medical cannabis use. These findings are in line with our previous study using a similar approach,15 except for city size and earlier onset age of initiating cannabis use, which were significant predictors in the previous study but not in the current one. This discrepancy could be due to different samples, as the previous study was aimed for more younger users as part of a national harm reduction project that developed a mini-intervention model and tools for professionals to discuss cannabis use with young people. The significant variables of the base model in this study remained significant in the extended model, indicating a stable association across models. The addition of variables in the extended model accounted for an additional 5% of the variance, increasing the total explained variance to 12%. Among the five new variables included in the extended model (i.e., employment status, severity of dependence, self-estimated THC and CBD content and the preferred type of cannabis usage), only the type of usage predicted medical cannabis use.
Our results are in line with previous studies which have also found that people who use and grow cannabis for medical motives are usually older, and female compared to recreational users.17,34,36,46,47 A possible explanation for this might be that women are more open to alternative treatments than men48,49 and that in general recreational cannabis use is more prevalent among men.13 According to Finnish population studies, males have used cannabis three times more prevalently compared to females in the past year and in the past month with lifetime prevalence for males in 2022 being 36% and for females 21%.13 In relation to age, there is a higher risk for experiencing chronic pain and diseases as people get older50 which could explain why medical users are on average older than recreational users. There are also several cannabis-specific factors in relation to age, like general health, risk of injuries and other medication use51 and relation to biological sex that show differences in physiology, use practices and experienced effects of cannabis use,52 but research findings on these factors are often inconsistent and require further inquiry.
The frequency of use is often found to be higher among medically motivated cannabis users,17,34,40,47,53 which was also found in our study even though there were no differences in the amount of cannabis used per session in the mainly medically motivated and mainly recreationally motivated groups. Most commonly reported amount across both groups was between 0.1 and 0.5 g, so our findings suggest similar usage patterns regarding the self-estimated quantity of cannabis consumed per session among both recreational and medical users (Table 1). The higher frequency of use among medically motivated users is understandable in the context of treating one’s perhaps chronic diseases. However, the medically motivated users did not on average use cannabis daily, perhaps taking breaks from their use to avoid building up tolerance for their self-medication. Future studies should look more closely at whether there are differences in terms of timing of use.47 It can be speculated that, for example, recreational users concentrate their use more for the weekends, while medical users might take weekends off from their use to lower their tolerance levels and experience more noticeable medical effects during the week to function better. A recent study based on Finnish national survey data partly confirms this as it found that medically motivated users distributed their use more evenly throughout the week compared to recreational users who mostly consumed on the evenings and weekends.38 Other differences were also found, including medical users using more often alone at home compared to recreational users, and recreational cannabis use occasions involved more often simultaneous alcohol use.38
While in our study there was a statistically significant difference between the groups in relation to employment status in the pairwise comparison (Table 1), this was no longer significant in the logistic regression model. This implies that there were other variables that are more important predictors for medical cannabis use than employment status, such as education level (lower education level predicted medical cannabis use). Other studies have not found statistically significant differences in education levels between recreational and medical users,34,41,46,47 which again could be due methodological differences, country-contexts, and populations. Based on national survey data, highly educated men have somewhat more prevalent past year cannabis use in Finland compared to middle and low education levels (9.1% vs. 6.8% and 6.5% respectively), but among women there are basically no educational differences.54 In general, education level has not been found to be associated with cannabis experimentation and use when other social background factors have been taken into account.12 Lower education level among medically motivated cannabis users in our sample could indicate for instance challenges in attaining desired education due to illness, or lower access to official health care services which could lead to increased self-medication with cannabis. However, these possibilities require more research.
To address the potential correlation between education and employment, we conducted additional analyses. Although education and employment were correlated, the correlation was weak to medium, suggesting that education is an independent predictor in our results.
Overall, pre-pandemic self-reported health was poorer among groups with lower education compared to groups with middle and higher education both in Finland55 and elsewhere in Europe56 but whether the association between lower education level and medical motive to use cannabis is due to poorer health status is speculative at this point. Besides education level there are several other factors involved when modeling the relation between socioeconomic status and health57 and how these relate to motivations around cannabis use is still unclear.
In addition to the statistically significant differences between the two groups, our study shows that factors like onset age of cannabis use, size of the city of residence, employment status, amount of use per session, severity of dependence, and self-estimated amounts of THC and CBD concentrations showed no statistically significant differences. Some of the associations observed in the pairwise comparisons (e.g., self-estimated THC content) were not significant in the logistic regression analysis, suggesting that higher THC content preferred by medical users in pairwise comparison is explained by the other factors, for instance the need for higher doses to treat their medical condition.39 Again, some of these associations, or their lack of, are found in other studies, while others are not. For instance, similar to the findings by Turna et al.,34 mainly medical users in our study reported using higher THC-containing products compared to recreational users. The high THC/low CBD concentration in Turna et al. was more for medical users who also endorsed recreational use. While higher THC concentrations have been associated with increases in adverse events for medical users and dependence and mental health problems for recreational users,39 medical users using higher THC products could also point to a therapeutic value of the psychoactive effects of cannabis, even though they are often considered side effects.14,58 However, there is no clear scientific consensus on what constitutes a high or low amount of THC for instance, even though attempts to set up a “standard unit” have been proposed.59 It is even more difficult to evaluate cannabinoid levels based on self-estimates of illegal products, as in the case of our study where over a quarter of the participants did not know the THC level of their most used cannabinoid product.
Regarding cannabis dependence, we did not find statistically significant difference between the groups on SDS scores. However, in the cross-country grower study by Snitzman et al.42 recreational only growers scored higher on SDS levels compared to the medical groups, with and without recreational motive. Sznitman47 also found licensed medical cannabis users scoring lower on The Cannabis Abuse Screening Test (CAST) compared to unlicensed medical users and recreational users. However, there are also studies showing medical users, especially those also endorsing recreational use, scoring higher on the Cannabis Use Disorder Identification Test – Revised (CUDIT-R) compared to recreational users and medical only users.34,46 As these different screening tests measure slightly different things it is difficult to draw direct conclusions from them, especially for people with medical motives as most current measurement tools are designed to screen problematic use.60 Overall, the dependence scores in the present study were relatively low in both groups, medical users scoring 1.69 while recreational users scored 1.94 on the scale from 0 to 12 (Table 1). All the groups in the study by Sznitman et al.42 had higher SDS scores compared to ours, with a total mean of 5.22 (SD = 1.77). This difference could partly be due to differences in samples as the study by Sznitman focused on comparing cannabis growers in 18 countries, but could also relate to the validity of screening tools which may not work equally well for different motivations of use.42,61 The issue of dependence is complex, especially regarding medical cannabis users who might depend on cannabis to treat their chronic disease, which does not equal problematic use or addiction.39,60
These empirical results are important to consider also in the wider context of cannabis use, both medical and recreational, which have long and complicated histories62 and sociology63 which vary depending on the local country-contexts. For instance, by the end of 2023 in the United States, 38 states, three territories, and the District of Columbia had allowed the medical use of cannabis products and 24 states had passed legislation to allow recreational adult use.64 Longitudinal studies looking at pre- and post-legalization of cannabis in, for example, California has shown that legislative changes have some impact on young adults who reported using cannabis for medical reasons prior to recreational laws and some of that association included transitioning from medical use to recreational use but also declined cannabis and other drug use after legalisation.65 In the USA between 1976 and 2016 reasons for cannabis use among high school students in general has changed from more social/recreational reasons toward coping with negative affects,66 perhaps indicating the rapidly changing cultural environment young people are experiencing. While state policies have an impact on for instance where people source their cannabis (legal store vs. illegal dealer),64 and potentially on the prevalence of self-reported use of cannabis for medical purposes,67 the full extent of public health impacts of cannabis policy reforms are challenging to evaluate in the United States context as it remains federally illegal.64
On the European level, these types of monitoring studies on cannabis use motivations are lacking as, for example, the European School Survey Project on Alcohol and Other Drugs (ESPAD) is focused on prevalence of use and perceived availability among adolescents. The European Web Survey on Drugs (EWSD) by the European Union Drugs Agency (EUDA, formerly known as European Monitoring Centre for Drugs and Drug Addiction, EMCDDA) is also more focused on reporting patterns of drug use and sources of supply than motivations.68 As an increasing number of European countries are reforming their cannabis policies, longitudinal monitoring of the public health impact these policy changes have across the EU on different user groups is needed,23 similar to the United States.64 Also due to complex pharmacology of cannabis and cannabinoids69,70 and the variety of products available64 future research on cannabinoid-based medicines and use in general can benefit from real-world evidence2,71 and from naturalistic studies with ecological validity to look at factors like reported motivations and experienced effects among different groups, alterations in consciousness, environment or setting of use and even placebo effects.39,72,73
While people continue to use cannabis for different reasons experiencing both benefits and harms, it is important to acknowledge that globally cannabis and cannabinoids are primarily used outside of official clinical contexts. Therefore, it is vital to investigate various factors involved to guide appropriate harm reduction and benefit increasement practices also outside of clinical use and to assist in designing future studies. Global and local cannabis policy reforms, with either medical or recreational motives, would also benefit from real-world evidence and from considering politicogenic drug effects, i.e., effects originating from modern drug policing.29 Current legislation in Finland for instance considers self-medicative use of cannabis as a criminal offense while there are notable challenges to access official medical-grade products via prescription despite it being legal for over 15 years. Whether medical intention to use cannabis is enough to make the practice medical is open for debate73 but taking into account the perspectives of people who have lived experiences using cannabis and cannabinoids for a variety of purposes is vital in this regard.
It is also important to note that even under recreational motivation there are several motivating factors like enjoyment, conformity, coping, experimentation, boredom, alcohol-related use, celebration, altered perception, social anxiety, relative low risk, sleep, and availability,74 which even among medical users are almost all positively inter-correlated with one another and affect, for instance, frequency of use.75 In addition, medical users have a plethora of indication-specific motivations to use cannabis and cannabinoids in personalized ways that offer the best symptom relief. Further studies are needed to include different motivational factors when studying motives of cannabis use and perhaps to go beyond the medical versus recreational distinction toward improvement as in the therapy versus enhancement debate.76 Studying how exogenous cannabinoids enter and affect the extended endocannabinoid system77 and understanding the interactions that follow has come a long way since the isolation of 9-delta-THC over 60 years ago78 but there remains a lot we do not yet know.
Limitations of the study
The study relied on self-reports about an activity which is illegal and stigmatized in Finland and this can entail a response bias. Estimating amounts used per session and frequency of use in the past month are also susceptible to recall bias. In addition, evaluating THC and CBD content from a product that is illegal to possess and use is practically impossible in Finland in the current policy climate. Even if some users had access to products that explicitly state THC and CBD levels, those can be inaccurately labeled,79 and even if accurate it is difficult to say what constitutes to the person consuming as low, moderate, or high level. Scientific scholars themselves are not fully agreeing what a standard dose of THC is, let alone CBD, CBN, CBG, and the other several hundred plant molecules that cannabis contains. In general, more sophisticated methods are needed to collect and evaluate real-world evidence about consumed cannabinoid contents of unlicensed non-medical grade products.
Cannabinoids are also often used concomitantly with other psychoactive substances, like alcohol and tobacco, and while both groups in this study scored low on severity of dependence scale in relation to cannabis use, we only included questions about cannabis and no other drug use which often differ between the groups.42,53 Screening tools to measure cannabis dependence in general have some variation which makes results challenging to compare between different studies and samples. People might not fully enclose their dependencies or problems with cannabis or other substance use33 limiting the reliability of such screening tools.
Another limitation of the current study pertains to the recruitment method. We recruited participants who at least sometimes use cannabis for self-medication, and although some recreational-only users also answered, these were a minority. The lack of a separately recruited recreational-only group may diminish the group differences. On the other hand, a separate recruitment for medical and recreational users might have led to over-emphasized group differences, whereas in the present study the two groups are sampled from a population that is assumedly more homogeneous, which could lessen the risk of exaggerating group differences.
Conclusions
Our study suggests that, compared to mainly recreational users, mainly medical users of cannabis are more likely to be less educated older females who use cannabis more often and in edible forms. On the other hand, there were also notable similarities between the two groups, such as similar employment status, starting cannabis use around the same time, using similar amounts per session with very similar estimated amounts of THC and CBD, and both groups scored relatively low on the severity of dependence. The results point to various statistically significant sociodemographic predictors and use practices which differentiate medical and recreational cannabis use motives. Beyond statistical significance or insignificance, our results provide a better understanding of people’s efforts to improve their current state using modern tools and technologies, like cannabinoids. These are relevant to consider when designing future studies as well as appropriate harm reduction and cannabis policy measures.
Resource availability
Lead contact
Aleksi Hupli (aleksi.hupli@tuni.fi).
Materials availability
This study did not generate new unique reagents.
Data and code availability
- •Data: all data reported in this paper will be shared by the lead contact upon request.
- •Data will be archived at the Finnish Social Science Data Archive after the completion of the research project in July 2025.
- •Code: all code used in this paper is archived at https://doi.org/10.5281/zenodo.15166960.
Acknowledgments
We gratefully acknowledge the two peer-reviewers for their helpful comments. A.H. gratefully acknowledges 10.13039/501100003125Finnish Cultural Foundation for supporting his academic work in a form of research grands (#00230439/#00240505). J.J. was funded by the 10.13039/501100005781Kone Foundation (#202105363).
Declaration of interests
The authors declare no competing interests.
STAR★Methods
Key resources table
| REAGENT or RESOURCE | SOURCE | IDENTIFIER |
|---|---|---|
| Software and algorithms | ||
| https://doi.org/10.5281/zenodo.15166960 | – | – |
Experimental model and study participant details
The online survey was aimed at Finnish people over the age of 18 and who had experience with at least occasionally using cannabis flower, oil or hash for self-medicative reasons without an official medical prescription. The survey was open from early April to mid-June in 2024 and the link to the survey was shared mainly on social media (Facebook, Instagram and X, formerly Twitter). Prior to the launching the survey the study protocol was approved by Tampere University Data Protection Office (on 4th of March 2024) based on the provided Data Protection Impact Assessment (DPIA) and by the Ethics Committee of the Tampere Region (decision number 20/2024). Survey participants included 348 males (64.8%), 153 females (28.5%), 22 individuals who identified as other gender (4.1%), and 14 who chose not to disclose their gender (2.6%). On average mainly recreational cannabis users were 35.2 years, while medical users were slightly older, with an average age of 38.5 years.
Method details
The survey sample was divided into two groups based on primary cannabis use motivation. Pairwise differences in demographic and use-related variables between the groups were examined with chi-square tests for categorical variables, and t-tests for continuous or ordinal variables. Next, logistic regression was used to examine the association between group membership (mainly recreational use coded as 0, mainly medical use coded as 1) and the various demographic and use-related factors.
Quantification and statistical analysis
For the logistic regression analysis, we build a base model and an extended model. For the models, we report odd ratios (OR), their 95% confidence intervals and p-values. To estimate the model fit, we report the Hosmer-Lemeshow goodness-of-fit test, Tjur’s R2, the likelihood ratio test (ANOVA), Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC). All the analyses were performed using the R (4.4.1) statistical software.