The impact of perceived social support and coping on distress in a sample of Atlantic Canadian health professional students during COVID-19 compared to pre-COVID peers
Bibliographic record
Abstract
PURPOSE: Students pursuing higher education and health professional (HP) programs (e.g., nursing, pharmacy, social work, medicine) experience stressors including academic pressures, workload, developing professional competencies, professional socialization, the hidden curriculum, entering clinical practice and navigating relationships with colleagues. Such stress can have detrimental effects on HP students physical and psychological functioning and can adversely affect patient care. This study examined the role of perceived social support and resilience in predicting distress of Atlantic Canadian HP students during the COVID-19 pandemic and compared the findings to a pre-COVID population of age and sex matched Canadians. METHOD: Second year HP students (N = 93) completed a survey assessing distress, perceived social support, and resilience and open-ended questions on student awareness of supports and counselling available to them, their use/barriers to the services, and the impact of COVID-19 on their personal functioning. HP student responses were also compared with age and sex matched Canadian peers from data collected prior to COVID-19. RESULTS: It was found that HP students reported moderate to severe psychological distress, and while they reported high levels of social support on a measure of perceived social support they also reported that the COVID-19 pandemic made them feel isolated and that they lacked social support. It was found that the sample of HP students reported significantly higher psychological distress than the mean scores of the age and sex matched sample of Canadian peers. CONCLUSIONS: These findings call for creation of more tailored interventions and supports for HP students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".