Addressing fidelity within complex health behaviour change interventions: A protocol of a scoping review of intervention fidelity frameworks and models.
Bibliographic record
Abstract
Intervention fidelity is crucial to facilitate accurate interpretation of research outcomes, but has been inadequately addressed within complex health behaviour change interventions. Recent research has highlighted a need for practical guidance to improve understanding and use of existing fidelity frameworks and models within complex health behaviour change intervention research. The aim of this paper is to present a protocol for a scoping review of existing intervention fidelity frameworks and models. In accordance with scoping review guidelines, the following stages will be conducted: (1) identifying the research question, (2) identifying potentially relevant studies of fidelity frameworks and models, (3) study screening and selection, (4) charting and extracting data from identified frameworks and models, (5) collating, summarising and reporting the results and (6) consultation with stakeholders. Two reviewers will independently conduct the screening and extraction stages. Identified frameworks will be collated, summarized and categorized iteratively by one reviewer in consultation with the review team. The findings of this review will provide a useful resource by identifying and comparing existing fidelity frameworks and models. It is intended that increased clarity and understanding in this area will facilitate the appropriate selection and application of fidelity frameworks for complex health behaviour change interventions, inform areas for future research, and ultimately contribute towards improving how intervention fidelity is addressed in this area.
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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 | 0.310 | 0.367 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.021 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".