Frameworks Used to Engage Postsecondary Students in Campus Mental Health Research: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: There is an increasing prevalence of mental health concerns reported among postsecondary students (PSS) and growing demands for care on campuses around the world, as such there is an urgent need for research and innovations in PSS mental health that engages PSS. However, best practices and guidelines for facilitating PSS engagement in research is lacking. To address this gap, we undertook this review to explore frameworks used for engaging with PSS in research focused on PSS mental health. METHODS: A scoping review of the academic literature was conducted. Frameworks used to engage PSS in mental health research were identified and categorized using the taxonomy of patient and public engagement by Greenhalgh et al. A list of barriers and facilitators to engaging with PSS was also identified and reported. RESULTS: Of the articles assessed for full-text screening (n = 167), 26 journal articles were included. Frameworks used for engaging PSS in mental health research were classified into one of the three categories from Greenhalgh et al.'s taxonomy: study-focused (n = 14), partnership-focused (n = 9) and power-focused (n = 3). No relevant frameworks were found for two categories: priority- and report-focused. Seven documents reported relational or process-related barriers and/or facilitators to engaging with PSS. Based on these findings, recommendations were drafted with PSS advisors on how to implement an engagement framework in PSS mental health research. CONCLUSIONS: We identified existing practices outlined within frameworks used to engage PSS and barriers and facilitators to engage with PSS in mental health research. Based on the review findings and PSS advisors recommendations, a need for developing a comprehensive engagement framework specific to the PSS context was identified. PATIENT OR PUBLIC CONTRIBUTION: The research team led consultations with a PSS advisory group for this review. Student advisors were actively engaged in data analysis, which included categorizing and drafting of recommendations, and the preparation of this manuscript.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".