The Consolidated Approach to Intervention Adaptation (CLARION): Developing and undertaking an empirically and theoretically driven intervention adaptation
Bibliographic record
Abstract
BACKGROUND: Intervention adaptation, the deliberate modification of the design or delivery of interventions to a new context, is more resource efficient than de novo development. However, adaptation must be approached methodically, as some modifications, such as those to the core components, may compromise the intervention's initial efficacy. While adaptation frameworks have been published, none have been identified as more likely to result in successful adaptations. Further, frameworks lack the step-by-step details needed for operationalization. Therefore, the goal of this paper is to share our experience in addressing these methodological limitations in intervention adaptation. The objectives were to describe: 1) our development of a step-by-step, theoretically and empirically driven approach to intervention adaptation labelled the ConsoLidated AppRoach to Intervention adaptatiON (CLARION), 2) the application of CLARION in adapting a depression self-management intervention, 3) the facilitators and challenges encountered when using CLARION. METHODS: The development of CLARION was informed by the Medical Research Council guidance, the Method for Program Adaptation through Community Engagement (M-PACE), and a published scoping review identifying the key steps in existing adaptation frameworks. M-PACE was selected for its patient-oriented research principles, its application to a similar complex intervention, and for offering some of the specificity needed for execution. However, the scoping review indicated that M-PACE lacked three critical steps: selecting a candidate intervention, understanding its core components, and pre-testing the adapted intervention. These were added to form CLARION, which was structured in two stages: the first involves selecting an intervention, identifying core components, and deciding on modifications; the second stage solicits interest stakeholder feedback to assess the acceptability of the preliminary adapted intervention (pre-test). RESULTS: Once CLARION was developed, it was put into action to adapt a depression self-management intervention. CLARION demonstrated several strengths: 1) clearly articulating core components before deciding on modifications, 2) mobilizing a diverse steering committee of experts, including patient partners and developers of the original intervention, which balanced input and efficiency, and 3) establishing committee decision-making rules prior to adjudication (specific criteria and 75% supermajority). Key challenges included defining the types of modifications requiring committee input, determining the extent of the committee's involvement, and prioritizing the presence of all committee members at meetings to avoid difficulties integrating incongruent feedback. CONCLUSIONS: The development of CLARION contributes to best practices for intervention adaptation by identifying step-by-step guidance as well as facilitators and barriers to its application.
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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 | 0.242 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".