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Record W4231760037 · doi:10.21203/rs.2.23235/v1

Applying an intersectionality lens to the Theoretical Domains Framework: a tool for thinking about how intersecting social identities and structures of power influence behaviour

2020· preprint· en· W4231760037 on OpenAlexafffund
Cole Etherington, Isabel B. Rodrigues, Lora Giangregorio, Ian D. Graham, Alison M. Hoens, Danielle Kasperavicius, Christine Kelly, Julia E. Moore, Matteo Ponzano, Justin Presseau, Kathryn M. Sibley, Sharon E. Straus

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of ManitobaUniversity of WaterlooUniversity of British ColumbiaOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsIntersectionalityContext (archaeology)Delphi methodThrough-the-lens meteringDelphiVotingIdentity (music)Computer scienceSociologyLens (geology)Political scienceEngineeringArtificial intelligenceGender studies

Abstract

fetched live from OpenAlex

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 and structures of power, 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. The modified Delphi involved two rounds of online voting followed by a final majority vote. 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. Conclusions Through an expert-consensus approach, we developed a tool for applying an intersectionality lens alongside the TDF. By considering the role of intersecting social factors when selecting, tailoring, and implementing KT interventions, they may become more effective.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.121
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.121
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.090
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0210.011
Science and technology studies0.0110.036
Scholarly communication0.0210.027
Open science0.0050.024
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.250
GPT teacher head0.556
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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