Program Evaluation of a Student-led Peer Support Service at a Canadian University
Bibliographic record
Abstract
Abstract Background: University/college campuses are a rigorous academic environment that also contain numerous financial, social and emotional stressors that often result in the deterioration of students’ mental health. The Peer Support Centre (PSC) is a pilot project that was established to provide peer support to students in these stressful conditions. We wanted to investigate whether peer support is a viable form of support that would benefit university students. The objective of this study is to determine whether the organization was indeed providing a beneficial service to students and if it was fulfilling the needs of the students that visited the service.Methods: After a support session, students (also referred to as supportees) were asked to complete an anonymous questionnaire regarding their self-reported mental wellbeing, their experience with previous professional mental health services, and their experience at the PSC. There weren’t any selection criteria for either the supportees or volunteers as the completion of the questionnaire was completely voluntary. Additionally, volunteers (also referred to as supporters) were asked to complete a similar questionnaire after conducting a support session. With the data collected from 1043 supportees and 797 volunteers from September 2016 – March 2020, a program evaluation was conducted for quality improvement purposes. The responses to the questionnaires were analysed by calculating the means, modes, standard deviations and performing Two-Sample t-tests.Results: The PSC is used by a wide variety of students of different sexes, genders, ethnicities. Students reported having a low ORS score, moderate anxiety as per the GAD-7 and moderate depression according to the PHQ-9. They find it easy to use and rely on it as an alternative form of support when they approach barriers that prevent them from accessing professional services. Lastly, the supporters feel very validated in their role and overall quite prepared and helpful when helping their fellow peers.Conclusions: The established of a service that delivers peer support would be beneficial to the students on a university or college campus as it can serve as a complement to professional services.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".