Advancing implementation frameworks with a mixed methods case study in child behavioral health
Bibliographic record
Abstract
Despite a growing policy push for the provision of services based on evidence, evidence-based treatments for children and youth with mental health challenges have poor uptake, yielding limited benefit. With a view to improving implementation in child behavioral health, we investigated a complementary implementation approach informed by three implementation frameworks in the context of implementing motivational interviewing in four child and youth behavioral health agencies: the Active Implementation Frameworks (AIF) (process), the Consolidated Framework for Implementation Research (factors), and the Implementation Outcomes Framework (evaluation). The study design was mixed methods with embedded interrupted time series and motivational interviewing (MI) fidelity was the primary outcome. Focus groups and field notes informed perspectives on the implementation approach, and a questionnaire explored the salience of Consolidated Framework for Implementation Research (CFIR) factors. Findings validate the process guidance provided by the AIF and highlight CIFR factors related to implementation success. Novel CFIR factors, not elsewhere reported in the literature, are identified that could potentially extend the framework if validated in future research. Introducing fidelity measurement in practice proved challenging and was not sustained beyond the study. A complementary implementation approach was successful in implementing MI in child behavioral health agencies. In contrast with the typical train and hope approach to implementation, practice change did not occur immediately post-training but emerged over a 7 month period of consultation and practice following a discrete interactive training period. The saliency of CFIR constructs aligned with findings from studies conducted in other contexts, demonstrating external validity and highlighting common factors that can focus planning and measurement.
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 | 0.078 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".