Examining Student Perceptions and Awareness of Social Prescribing in a Canadian University
Bibliographic record
Abstract
Introduction: As the social prescribing movement grows in Canada, there is a need to understand whether students (a) are aware of this concept and (b) think it is important. Objectives: The aim of this study was to examine student perceptions and awareness of social prescribing in a Canadian university. Methods: This study was conducted at Brock University. Data collection took place between March and June of 2023. Eligibility criteria included (a) being a student at Brock University and (b) being able to read and write English. Participants completed an online survey. Quantitative data analysis consisted of basic mathematical computations. Qualitative data analysis consisted of qualitative content analysis. Results: Most participants (138/183, 75.4%) had never heard about social prescribing before. Once they were told what social prescribing is, the vast majority of participants thought that it seemed like a useful way to support health and well-being (121/126, 96.0%), felt that offering it on campus would be beneficial (129/130, 99.2%), believed that it is relevant to future health care professionals (121/128, 94.5%), thought that it should be part of the curriculum for health sciences programs (118/128, 92.2%), and expressed that they would be willing to follow their physician’s advice if they were to suggest social prescribing at an appointment (119/126, 94.4%). Conclusion/Discussion: Given the lack of awareness about social prescribing and the positive perceptions about this concept among the student body, Canadian post-secondary institutions are encouraged to (a) integrate this concept into the curriculum for health sciences programs and (b) offer social prescribing on campus.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".