Focusing a realist evaluation of peer support for paediatric mental health
Bibliographic record
Abstract
OBJECTIVE: Mental health problems are a leading and increasing cause of health-related burden in children across the world. Peer support interventions are increasingly used to meet this need using the lived experience of people with a history of mental health problems. However, much of the research underpinning this work has focused on adults, leaving a gap in knowledge about how these complex interventions may work for different children in different circumstances. Realist research may help us to understand how such complex interventions may trigger different mechanisms to produce different outcomes in children. This paper reports on an important first step in realist research, namely the construction of an embryonic initial programme theory to help 'focus' realist evaluation exploring how children's peer support services work in different contexts to produce different outcomes in the West Midlands (UK). METHODS: A survey and preliminary semi-structured realist interviews were conducted with 10 people involved in the delivery of peer support services. Realist analysis was carried out to produce context-mechanism-outcome configurations (CMOC). RESULTS: Analysis produced an initial programme theory of peer support for children's mental health. This included 12 CMOCs. Important outcomes identified by peer support staff included hope, service engagement, wellbeing, resilience, and confidence; each generated by different mechanisms including contextualisation of psychoeducation, navigating barriers to accessing services, validation, skill development, therapeutic relationship, empowerment, and reducing stigma. CONCLUSION: These data lay the groundwork for designing youth mental health realist research to evaluate with nuance the complexities of what components of peer support work for whom in varying circumstances.
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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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".