Work-integrated learning and the importance of peer support and sense of belonging
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the relationship between peer support and sense of belonging on the mental health and overall well-being, with a specific focus on comparing the perceptions of students in a work-integrated learning (WIL) program to those in a traditional non-WIL program. Design/methodology/approach Semi-structured group interviews were conducted with 25 participants, selected from a university with a WIL program. Interview data captured perceptions of peer support, sense of belonging, and how these influenced mental health, overall well-being, and confidence in making school-to-work transitions. Analysis followed the grounded theory approach of Glaser. Findings The analysis revealed that peer support and sense of belonging were essential protective factors for university student’s mental health and well-being, particularly during off-campus work terms or when transitioning to the labor market after graduation. Data suggested that participating in a WIL program can exacerbate students’ perceived barriers to accessing peer support resources and, in turn, lead to poor mental health. Originality/value The findings provide evidence for the importance of peer support and sense of belonging on mental health and help-seeking behaviors. Findings are important for the development of health programs, initiatives, and policies, particularly in light of the increase in mental illness amongst university students during their studies and as they prepare for the competitive labor market after graduation.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".