Discharge from secondary care services to primary care for adults with serious mental illness: a scoping review
Bibliographic record
Abstract
BACKGROUND: Effective transitions of patients from Secondary Care Services (SCSs) to primary care are necessary for optimization of resources and care. Factors that enable or restrict smooth transitions of individuals with Serious Mental Illness (SMI) to primary care from SCSs have not been comprehensively synthesized. METHODS: A scoping review was conducted to answer the questions (1) "What are the barriers and facilitators to discharge from SCSs to primary care for adults with SMI?" and (2) "What programs have been developed to support these transitions?". RESULTS: Barriers and facilitators of discharge included patient-, primary care capacity-, and transition Process/Support-related factors. Patient-related barriers and facilitators were most frequently reported. 11 discharge programs were reported across the evidence sources. The most frequently reported program components were the provision of additional mental health supports for the transition and development of care plans with relapse signatures and intervention plans. CONCLUSIONS: Established discharge programs should be comprehensively evaluated to determine their relative benefits. Furthermore, research should be expanded to evaluate barriers and facilitators to discharge and discharge programs in different national contexts and models of care. TRIAL REGISTRATION: The protocol for this scoping review is registered with the Open Science Framework ( https://doi.org/10.17605/OSF.IO/NBTMZ ).
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".