Exploration of Factors Affecting Post-Secondary Students’ Stress and Academic Success: Application of the Socio-Ecological Model for Health Promotion
Bibliographic record
Abstract
OVERVIEW: There has been an increase in the frequency and severity of stress experienced by Canadian post-secondary students, which has adverse implications on their academic success. This work applied the socio-ecological model for health promotion to explore the contextual factors that influence this relationship at the individual, interpersonal, institutional, community, and public policy levels. METHODS: Using a qualitative, phenomenological approach, we conducted 38 semi-structured interviews with undergraduate students and on-campus staff who provide services to this population at a post-secondary institution in Southwestern Ontario, Canada. Thematic analysis inductively identified overarching themes among participants' perspectives. RESULTS: Several positive and negative factors were identified at each socio-ecological model level, demonstrating the complex interplay of demographic, psychological, emotional, social, physical, and academic factors impacting students' academic stress. CONCLUSIONS: A lack of communication and knowledge seems to underlie many factors, highlighting the need to strengthen communication strategies to promote awareness, accessibility, and availability of services and programs on campus. Results also pointed to focusing on proactive, resilience-focused, upstream mental health promotion efforts at post-secondary institutions to reduce stress and improve academic success. This knowledge can help Canadian campuses better address students' needs.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".