Fidelity of intervention delivery in psychosocial and behavioral programs (FIPP): A modified Delphi study and final guideline
Bibliographic record
Abstract
Introduction: Fidelity - ensuring interventions are implemented as intended - is a key focus in implementation science. Despite its benefits in research and practice, data on the fidelity of implementation are often overlooked, measured inconsistently, or underreported. In 2024, we proposed a preliminary guideline for one component of fidelity - the fidelity of delivery in parenting interventions. This study builds upon that work, refining the guideline for psychosocial and behavioral interventions. Methods: = 5) resulting in six rounds of iterative revisions to produce the final FIPP. Results: The modified Delphi technique resulted in a final FIPP with 35 items across six categories: intervention, facilitator, fidelity measure, and fidelity assessor characteristics; fidelity assessment method; and fidelity results and discussion. The final FIPP was produced based on engagement and data from the survey participants, consensus meeting panelists, and email panelists. Conclusions: This study advances reporting on fidelity of delivery in psychosocial and behavioral interventions by refining the FIPP guideline through a rigorous, consensus-driven process. The FIPP provides a comprehensive structure to improve the consistency and transparency of fidelity of delivery assessment. By promoting standardized reporting, the FIPP enhances the quality of implementation science, ultimately supporting more effective interventions and better participant outcomes. Researchers and practitioners are encouraged to adopt the FIPP to strengthen intervention fidelity and drive meaningful progress in the field.
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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 | 0.271 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".