Development of a method for Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI): a modified Delphi study
Bibliographic record
Abstract
BACKGROUND: Intervention adaptation is often necessary to improve the fit between evidence-based practices/programs and implementation contexts. Existing frameworks describe intervention adaptation processes but do not provide detailed steps for prospectively designing adaptations, are designed for researchers, and require substantial time and resources to complete. A pragmatic approach to guide implementers through developing and assessing adaptations in local contexts is needed. The goal of this project was to develop Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI), a method for intervention adaptation that leverages human centered design methods and is tailored to the needs of intervention implementers working in applied settings with limited time and resources. METHOD: MODIFI was iteratively developed via a mixed-methods modified Delphi process. Feedback was collected from 43 implementation research and practice experts. Two rounds of data collection gathered quantitative ratings of acceptability and inclusion (Round 1) and feasibility (Round 2), as well as qualitative feedback regarding MODIFI revisions analyzed using conventional content analysis. RESULTS: In Round 1, most participants rated all proposed components as essential but identified important avenues for revision which were incorporated into MODIFI prior to Round 2. Round 2 emphasized feasibility, where ratings were generally high and fewer substantive revisions were recommended. Round 2 changes largely surrounded operationalization of terms/processes and sequencing of content. Results include a detailed presentation of the final version of the three-step MODIFI method (Step 1: Learn about the users, local context, and intervention; Step 2: Adapt the intervention; Step 3: Evaluate the adaptation) along with a case example of its application. DISCUSSION: MODIFI is a pragmatic method that was developed to extend the contributions of other research-based adaptation theories, models, and frameworks while integrating methods that are tailored to the needs of intervention implementers. Guiding teams to tailor evidence-based interventions to their local context may extend for whom, where, and under what conditions an intervention can be effective.
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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 | 0.233 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".