Challenges of remote working, perceived peer support, mental health and well-being of WIL students
Bibliographic record
Abstract
Purpose This study explored the relationship between perceived peer support, mental health and the well-being of students enrolled in work-integrated learning (WIL) at a Canadian institute of higher education, who were completing remote work experiences. Design/methodology/approach An online survey and virtual semi-structured interviews were used to collect data. The online survey captured demographic information as well as measures of perceived peer support, loneliness, positive mental health (PMH) and stressors associated with the on-going pandemic. The interviews captured narratives regarding peer support, attitudes surrounding remote work, mental health and well-being. Findings WIL students completing remote work terms experienced only moderate levels of peer support, moderate loneliness, below-average PMH and all the stressors associated with the on-going pandemic. Data also revealed that completing a remote work term negatively impacted work communications, opportunities to build connections with colleagues and overall motivation. On the other hand, WIL students appreciated the flexibility and comfort of working from home, as well as reduced work-related expenses. Originality/value With remote work experiences increasing globally and now a reality for many WIL students, the potential negative effects emphasize the importance of providing social and mental health support and resources, especially during stressful times.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".