Peer Review Report For: Crisis care for children and young people with mental health problems: national mapping, models of delivery, sustainability and experience (CAMH-Crisis2). A study protocol. [version 2; peer review: 3 approved]
Bibliographic record
Abstract
Background One in six five 16-year-olds have a probable mental health difficulty. Of these, almost half of older teenagers and a quarter of 11–16-year-olds report having self-harmed or attempted suicide. Currently, there is little research into mental health crisis services for young people, with little understanding of what services exist, who uses them, or what works best. Question ‘How are mental health crisis responses for children and young people up to the age of 25 sustained, experienced and integrated within their local systems of services’? Objectives To describe National Health Service (NHS), local authority, education and third sector approaches to the implementation and organisation of crisis care for children and young people across England and Wales. To identify eight contrasting case studies in which to evaluate how crisis services have developed and are currently organised, sustained, experienced and integrated within the context of their local systems of services. To compare and contrast these services in the context of the available international evidence, drawing out and disseminating clear implications for the design and delivery of future crisis responses for children and young people and their families. Methods A sequential mixed methods approach, underpinned by normalisation process theory will be employed. A survey will create a detailed record of how crisis responses across England and Wales are organised, implemented and used. Subsequently, eight contrasting services in relation to geographic and socioeconomic setting, populations served, and service configuration will be identified as case studies. Interviews will be conducted with children, young people and parents/carers who have used the service, as well as commissioners, managers and practitioners. Operational policies and service usage data will also be examined. Analysis of how each service is provided, experienced, implemented and sustained will be conducted both inductively and deductively, reflecting normalisation process theory constructs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.388 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.457 | 0.252 |
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".