Fidelity is not easy! Challenges and guidelines for assessing fidelity in complex interventions
Bibliographic record
Abstract
BACKGROUND: Fidelity in complex behavioural interventions is underexplored and few comprehensive or detailed fidelity studies report on specific procedures for monitoring fidelity. Using Bellg's popular Treatment Fidelity model, this paper aims to increase understanding of how to practically and comprehensively assess fidelity in complex, group-level, interventions. APPROACH AND LESSONS LEARNED: Drawing on our experience using a mixed methods approach to assess fidelity in the INFORM study (Improving Nursing home care through Feedback On perfoRMance data-INFORM), we report on challenges and adaptations experienced with our fidelity assessment approach and lessons learned. Six fidelity assessment challenges were identified: (1) the need to develop succinct tools to measure fidelity given tools tend to be intervention specific, (2) determining which components of fidelity (delivery, receipt, enactment) to emphasize, (3) unit of analysis considerations in group-level interventions, (4) missing data problems, (5) how to respond to and treat fidelity 'failures' and 'deviations' and lack of an overall fidelity assessment scheme, and (6) ensuring fidelity assessment doesn't threaten internal validity. RECOMMENDATIONS AND CONCLUSIONS: Six guidelines, primarily applicable to group-level studies of complex interventions, are described to help address conceptual, methodological, and practical challenges with fidelity assessment in pragmatic trials. The current study offers guidance to researchers regarding key practical, methodological, and conceptual challenges associated with assessing fidelity in pragmatic trials. Greater attention to fidelity assessment and publication of fidelity results through detailed studies such as this one is critical for improving the quality of fidelity studies and, ultimately, the utility of published trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT02695836. Registered on February 24, 2016.
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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 | 0.442 | 0.690 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".