Addressing fidelity within complex health behaviour change interventions: a scoping review of fidelity frameworks and models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it