Barriers experienced by undergraduate students to access to mental health services: Results from a Canadian study
Bibliographic record
Abstract
This study examined the experiences of Canadian undergraduate students accessing mental healthcare between November 2022 to February 2023. We specifically assessed the impact of social determinants of health (i.e., gender, socioeconomic status, immigration status, English as a second language). Participants were recruited through social media platforms and by undergraduate program administrators at Canadian universities. Participants were asked to provide demographic information, answer questions about their experiences accessing mental healthcare, and to complete the mental health continuum short form (MHC-SF). Descriptive statistics and linear regression models were used to assess the association between MHC-SF and social determinants of health (e.g.: demographics, language, immigration status). Of 1098 students invited to participate, 365 participants completed the study (completion rate: 33.2%). Their mean age (SD) was 21.4 (4.6) years; 73.6% were female and 45.7% identified as non-White. Overall, the mean (SD) MHC-SF score of participants was 2.36 (0.99) out of 5. Students with low SES had lower MHC-SF scores (mean 2.08 vs 2.45; p = 0.003). The multivariable analysis showed that low SES (β -0.36; 95%CI: -0.60 to -0.12) and female gender (β -0.29; 95%CI: -0.58 to -0.012) were associated with lower MHC-SF scores. Additionally, being White was associated with higher MHC-SF scores (β -0.29; 95%CI: -0.44 to 0.54). Age, English as a second language, and immigration status were not significant predictors of mental health. High levels of stress, negative perceptions of the mental healthcare system, and limited access were the more common reported themes in the qualitative analysis. In our cohort, university students from across Canada had low MHC scores. Social determinants of health (e.g., low SES, being non-White, and identifying as a woman) were independent predictors of low MCH scores. Further studies are needed to identify specific groups at higher risk as well as strategies to overcome the suboptimal mental health among Canadian students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".