Applying an intersectionality lens to the Theoretical Domains Framework: a tool for thinking about how intersecting social identities and structures of power influence behaviour
Bibliographic record
Abstract
Abstract Background: A key component of the implementation process is identifying potential barriers and facilitators that need to be addressed. The Theoretical Domains Framework (TDF) is one of the most commonly used frameworks for this purpose. When applying the TDF, it is critical to understand the context in which behaviours occur. Intersectionality, which accounts for the interface between social identity factors (e.g. age, gender) and structures of power (e.g. ageism, sexism), offers a novel approach to understanding how context shapes individual decision-making and behaviour. We aimed to develop a tool to be used alongside applications of the TDF to incorporate an intersectionality lens when identifying implementation barriers and enablers. Methods: An interdisciplinary Framework Committee (n=17) prioritized the TDF as one of three models, theories, and frameworks (MTFs) to enhance with an intersectional lens through a modified Delphi approach. In collaboration with the wider Framework Committee, a subgroup considered all 14 TDF domains and iteratively developed recommendations for incorporating intersectionality considerations within the and its domains TDF. An iterative approach aimed at building consensus was used to finalize recommendations.Results: Consensus on how to apply an intersectionality lens to the TDF was achieved after 12 rounds of revision. Two overarching considerations for using the intersectionality-enhanced TDF were developed by the group as well as two to four prompts for each TDF domain to guide interview topic guides. Considerations and prompts were designed to assist users to reflect on how individual identities and structures of power may play a role in barriers and facilitators to behaviour change and subsequent intervention implementation. Conclusions: Through an expert-consensus approach, we developed a tool for applying an intersectionality lens alongside the TDF. Considering the role of intersecting social factors when identifying barriers and facilitators to implementing research evidence may result in more targeted and effective interventions that better reflect the realities of those involved.
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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 | 0.151 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".