The Impact of a Peer Support Program on the Social and Emotional Wellbeing of Postgraduate Health Students During COVID-19: A Qualitative Study
Bibliographic record
Abstract
Peer support is a widely adopted strategy in higher education, facilitating student engagement in socially safe groups to enhance knowledge and social skills. While its benefits are recognized during in-person education, evidence supporting these benefits in an online format of study, especially among postgraduate health students, remains scarce. This study explored the impact of a peersupport program on the social and emotional well-being of postgraduate health students who were studying online during the COVID-19 pandemic. Peer support groups were implemented for a mixed group of local and international students enrolled in a postgraduate health subject delivered online in 2021 at Western Sydney University, Australia. Data were collected using four focus group discussions conducted via Zoom, transcribed verbatim, translated (as required), and analyzed through inductive thematic analysis. Three major themes were identified: (i) emotional well-being and social support; (ii) social interactions and forming friendships; and (iii) facilitators and barriers to engagement. This study highlights the positive impact of the peer support program in enhancing social and emotional well-being among post-graduate health students, with most being international students. Despite the challenges posed by online learning during COVID-19, students experienced significant social, emotional, and cultural benefits from participating in the peer support program.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".