Framework for successful school reintegration after psychiatric hospitalization: A systematic synthesis of expert recommendations
Bibliographic record
Abstract
Abstract This systematic synthesis aimed to identify and synthesize expert recommendations from best available clinical and scientific literature for successful school reintegration of students after psychiatric hospitalization. Following principles outlined by the Evidence for Policy and Practice Information and Coordinating Centre (EPPI‐Centre), we searched 15 electronic databases with all possible literature from 1985 to May 2019 and conducted both supplementary retrospective and prospective reference searches. Fifty‐three documents (37 scientific and 16 clinical) met the inclusion criteria. A thematic synthesis of identified recommendations led to the development and definition of a nine‐step framework to guide collaboration between school and mental health practitioners. This innovative framework offers clear, structured and consensus‐based prescriptive guidelines to determine what should be done, for whom, by whom, when, and how, to facilitate the school reintegration of students hospitalized for mental health issues. Additional studies are necessary to evaluate the implementation and effectiveness of this step‐based framework.
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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 | 0.116 | 0.221 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.037 | 0.016 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".