Treatment fidelity in psychosocial intervention for children and adolescents with comorbid problems
Bibliographic record
Abstract
BACKGROUND: Intervention fidelity has important implications for the interpretation of intervention outcomes. Reviews on fidelity implementation for psychosocial interventions targeting children and adolescents with comorbid mental health problems are scarce. The purpose of this study was to systematically review reported fidelity of psychosocial interventions for children with comorbid mental health conditions. METHOD: Fidelity and quality ratings were calculated based on an analysis of articles resulting from a previously reported systematic search of the literature (using PsycINFO, MEDLINE and ERIC databases between 1994 and 2009), using the Intervention Fidelity Assessment Checklist for the fidelity measure and the Cochrane Collaboration's tool for assessing risk of bias for the quality measure. RESULTS: Overall, few studies were found to have a high level of fidelity adherence. Only 1 of the 10 studies met the 'high' intervention fidelity cutoff. CONCLUSIONS: Findings suggest that current psychosocial interventions for children and adolescents with comorbid mental health disorders must be interpreted with caution, given many studies either do not measure intervention fidelity or have variable levels of fidelity adherence. Including fidelity components in future studies would aid in determining the effectiveness and generalizability of interventions targeted at children with comorbid disorders.
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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 | 0.041 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".