Developing the intersectionality supplemented Consolidated Framework for Implementation Research (CFIR) and tools for intersectionality considerations
Bibliographic record
Abstract
Abstract Background The concept of intersectionality proposes that demographic and social constructs intersect with larger social structures of oppression and privilege to shape experiences. While intersectionality is a widely accepted concept in feminist and gender studies, there has been little attempt to use this lens in implementation science. We aimed to supplement the Consolidated Framework for Implementation Research (CFIR), a commonly used framework in implementation science, to support the incorporation of intersectionality in implementation science projects by (1) integrating an intersectional lens to the CFIR; and (2) developing a tool for researchers to be used alongside the updated framework. Methods Using a nominal group technique, an interdisciplinary framework committee (n = 17) prioritized the CFIR as one of three implementation science models, theories, and frameworks to supplement with intersectionality considerations; the modification of the other two frameworks are described in other papers. The CFIR subgroup (n = 7) reviewed the five domains and 26 constructs in the CFIR and prioritized domains and constructs for supplementation with intersectional considerations. The subgroup then iteratively developed recommendations and prompts for incorporating an intersectional approach within the prioritized domains and constructs. We developed recommendations and prompts to help researchers consider how personal identities and power structures may affect the facilitators and inhibitors of behavior change and the implementation of subsequent interventions. Results We achieved consensus on how to apply an intersectional lens to CFIR after six rounds of meetings. The final intersectionality supplemented CFIR includes the five original domains, and 28 constructs; the outer systems and structures and the outer cultures constructs were added to the outer setting domain. Intersectionality prompts were added to 13 of the 28 constructs. Conclusion Through an expert-consensus approach, we modified the CFIR to include intersectionality considerations and developed a tool with prompts to help implementation users apply an intersectional lens using the updated framework.
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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 | 0.486 | 0.464 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.011 | 0.027 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".