Access to best-evidenced mental health support for care-experienced young people: Learnings from the implementation of cognitive therapy for PTSD
Bibliographic record
Abstract
Objectives: Rates of PTSD are up to 12 times higher in care-experienced young people (CEYP) compared to their peers. Trauma-focused CBTs are the best-evidenced treatment for youth with PTSD, yet, in practice CEYP often struggle to access this treatment. We worked alongside services to understand barriers and facilitators of the implementation of cognitive therapy for PTSD (a type of tf-CBT) to CEYP. Design: This was an active open implementation trial. Methods: We recruited 28 mental health teams across England, including general CAMHS, targeted-CAMHS for CEYP, and social care based teams. From these teams, participants were 243 mental health professionals, from a wide variety of professional backgrounds. Following recruitment/intervention training, teams participated in rolling 3-monthly focus groups and individual interviews, to understand what helped and hindered implementation. Data were analysed using a framework analysis conducted using the CFIR 2.0. Results: Almost half of the teams were able to implement, but only approximately one quarter with CEYP, specifically. Universal barriers that were discussed by almost all teams, particularly highlighted service-structures and commissioning as a major barrier delivery to CEYP, as well as the complexities of the young person and their network. Unique factors that differentiated teams who did and did not implement included the culture of the team, leadership engagement and style, and the development of in-house supervision structures. Conclusions: Findings offer key considerations for mental health teams, service leads, commissioners and policy-makers, to enhance delivery of best-evidenced mental health treatments like CT-PTSD, for CEYP.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".