Students’ experiences with school reintegration after psychiatric hospitalization: a scoping review
Bibliographic record
Abstract
Students with severe mental health issues may require inpatient psychiatric care, requiring them to leave school temporarily. For a myriad of reasons, reintegration back into the classroom after mental health hospitalization is often difficult and can put students at heightened risk for readmission and exacerbation of psychiatric symptoms. Unfortunately, there is a lack of understanding of students' experiences with this hospital-to-school transition. This scoping review aimed to identify factors that students perceived to be facilitators and barriers to school reintegration. This scoping review followed the methodological framework outlined by Arksey and O'Malley and Levac et al. Ten documents were included in the final review. Study characteristics, key demographics, and students' perceived facilitators and barriers to school reintegration after psychiatric hospitalization were extracted, and the latter were examined using deductive content analysis. We employed the ecological systems theory as a framework for this analysis. This scoping review identified four factors that students perceived as both facilitators and barriers in their transition back to school: psychiatric symptomology, connection to peers, connection to school adults, and academic planning and accommodations, and two factors students perceived to be distinct facilitators: caregivers as advocates and school mental health supports. Findings are limited by a lack of peer-reviewed research in this area. Further research that includes students at all stages of education is needed, especially those attending postsecondary institutions.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".