26 Supporting the Transition to Postsecondary for Students with Mental Health Conditions: A Scoping Review
Bibliographic record
Abstract
Abstract Primary Subject area Mental Health Background The transition from high school to postsecondary is a critical milestone for independence and empowerment. This life stage frequently coincides with the emergence of most mental health conditions (MHCs). Without adequate support to assist with the transition to postsecondary education, the mental health of arriving students with existing MHCs is likely to decline or remain unmet. Declining mental health is strongly associated with students withdrawing from both secondary and postsecondary education. However, a scoping review of interventions aiming to support youth with MHCs transition to postsecondary has not been conducted. Objectives The objectives of this scoping review were to identify: (1) researched interventions that support youth with MHCs during the transition to postsecondary; (2) best practices used to support this transition; (3) methods of evaluating these interventions and any limitations; and (4) gaps where future research is warranted. Design/Methods A database search of MEDLINE, PsycINFO, Embase, SocINDEX, ERIC, CINHAL, and Education Research Complete was undertaken. Two reviewers independently screened studies and extracted the data. Thematic analysis and risk-of-bias assessment were conducted on included studies. Results Nine studies were included in this review, describing eight unique interventions (Figure 1). Sixty-two percent of interventions were nonspecific in the MHCs that they were targeting in postsecondary students. These interventions were designed to support students upon arrival to postsecondary. Peer mentorship, student engagement, and interagency collaboration were found to be beneficial approaches to supporting youth transitioning into postsecondary (Table 1). The overall quality and level of evidence in these studies was low. Three knowledge gaps were found: evidence was not generalizable to the diversity of MHCs, intervention studies were mostly cross-sectional in nature and lacked follow-up data, and sustaining intervention funding remained a challenge for postsecondary institutions. Conclusion The volume of research identified was limited but indicated overall that offering support during the transition to postsecondary was beneficial for students with MHCs. Further evidence is needed that is generalizable across the mental health spectrum, and that assesses intervention outcomes in relation to intervention costs.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".