Addressing fidelity within complex health behaviour change interventions: a scoping review of fidelity frameworks and models
Bibliographic record
Abstract
Fidelity is an important but under-addressed aspect of health behaviour change intervention research. Consensus is lacking regarding terminology, definitions, and conceptualisations. Fidelity frameworks and models can help people address fidelity in a structured way and ensure clarity and consistency of terminology, but they are underutilised to date. We aimed to identify and describe existing fidelity frameworks/models and compare these in terms of fidelity constructs included. We conducted a scoping review using a pre-specified search, dual independent screening, and data extraction. We analysed data using basic descriptive statistics and qualitative content analysis. We identified 20 fidelity frameworks/models. All frameworks/models included constructs relating to intervention delivery. All frameworks/models also included additional constructs; however, there was a lack of consensus across these, and whether they are components or moderators of fidelity. For health behaviour change researchers wishing to address fidelity, selecting a comprehensive framework/model that facilitates consideration of multiple constructs and that aligns with their intended purpose and context may be beneficial. Fidelity is a multi-faceted concept of which delivery is an important, but not the only, construct. Findings will help researchers consider fidelity in greater depth, apply and refine existing frameworks/models, and improve how fidelity is addressed in future behavioural interventions.
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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 | 0.164 | 0.356 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".