Post‐secondary student transitions and mental health: Literature review and synthesis
Bibliographic record
Abstract
Abstract Waning mental health and resilience in the post‐secondary student population is a growing concern across North American institutions, these concerns have only been compounded further by the added stressors associated with the COVID 19 pandemic. Transitioning into post‐secondary brings with it a variety of interpersonal and intrapersonal challenges that often reciprocally influence each other (e.g., moving away from existing social support networks, forming new relationships, increased responsibility, and financial independence, increased academic expectations, etc.). Successful adaptation to such challenges is equally influenced by demographic (e.g., impacts of gender, sexuality, ethnicity, and socioeconomic status) and institutional factors (e.g., the provision and efficacy of health‐related services and programming on campus). A thorough literature review and synthesis was conducted examining post‐secondary student mental health. Attention was given to post‐secondary mental health, help seeking, demographic, and institutional characteristics. The scope of this literature review focused on the North American context. Future directions for research and practice are drawn from the findings. Institutions need to focus on initiatives intended to improve campus climate and service utilization amongst their students. Health care providers, administrators, and educators are challenged to provide evidence‐based, health‐related services that meet the unique needs of their student population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".