Overcoming challenges of embedding child and youth mental health research in community NHS services
Bibliographic record
Abstract
Purpose This paper aims to examine the barriers and facilitators of conducting and implementing research in frontline child and youth mental health settings in the UK. Design/methodology/approach Researchers, clinicians and commissioners who attended a workshop at the Big Emerging Minds Summit in October 2022 provided their expert views on the structural barriers and possible solutions to integrating research in clinical practice based on their experiences of child and young people mental health research. Findings The identified barriers encompass resource constraints, administrative burdens and misalignment of research priorities, necessitating concerted efforts to foster a research-supportive culture. This paper proposes the potential actionable solutions aimed at overcoming challenges, which are likely applicable across various other health-care systems and frontline NHS services. Solutions include ways to bridge the gap between research and practice, changing perceptions of research, inclusive engagement and collaboration, streamlining ethics processes, empowering observational research and tailored communication strategies. Case examples are outlined to substantiate the themes presented and highlight successful research initiatives within NHS Trusts. Originality/value This paper provides an insight into the views of stakeholders in child and youth mental health. The themes will hopefully support and influence clinicians and academics to come together to improve the integration of research into clinical practice with the hope of improving service provision and outcomes for our children and young people.
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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 | 0.387 | 0.308 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.047 |
| Scholarly communication | 0.036 | 0.022 |
| Open science | 0.008 | 0.057 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".