The mental health detention process: a scoping review to inform GP training
Bibliographic record
Abstract
BACKGROUND: GPs are often faced with deciding whether or not a patient may require detention for assessment in hospital under mental health legislation. This can be a complex and daunting process. Despite this, GPs and most other professionals receive limited formal training. AIM: To map and review the current literature on training in mental health detention processes. These insights are vital to inform the further development of meaningful educational approaches. DESIGN & SETTING: A systematic scoping literature review was conducted to identify what is known about how best to develop training in this area. METHOD: Arksey and O'Malley's framework was used to select, chart, and analyse articles from across six electronic databases. A total of 1136 articles were included in the initial screening phase and 183 articles were included in the full-text screening phase. Key themes were derived using an iterative and thematic approach. A personal and public involvement (PPI) group was set up for this project and other stakeholders in the mental health detention process were consulted about the findings. RESULTS: Fifty-two articles were included in the final review. Professionals consistently highlighted unmet training needs and difficulties with the process. There were identified needs for practical, interdisciplinary training, including discussion of complex cases, and opportunities to learn from those with direct experience. CONCLUSION: This work is foundational for the development of meaningful educational approaches around mental health detention processes. A strong research base will inform and strengthen training with the ultimate aim of improving patient care.
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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 | 0.066 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.045 | 0.037 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".