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Record W4414491486 · doi:10.1093/tbm/ibaf046

Using the behavior change technique ontology to characterize the content of implementation strategies: a secondary analysis of 151 trials targeting evidence-based nursing practice

2025· article· en· W4414491486 on OpenAlex
Charlene Weight, Rachael Laritz, Meagan Mooney, Billy Vinette, Sonia Angela Castiglione, Nicola Straiton, Gabrielle Chicoine, Shuang Liang, Kristin J. Konnyu, Marie‐Pierre Gagnon, Sonia Semenic, Sandy Middleton, Natalie Taylor, Vasiliki Bitzas, Nathalie Folch, B. Vachon, Geneviève Rouleau, Andrea M. Patey, Nicola McCleary, Joshua Porat‐Dahlerbruch, Guillaume Fontaine

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTranslational Behavioral Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsKensington HealthOttawa HospitalInstitute for Work & HealthInstitute of Health Services and Policy ResearchUniversity of TorontoUniversité du Québec en OutaouaisJewish General HospitalSt. Michael's HospitalInstitut Universitaire en Santé Mentale de QuébecCentre Hospitalier de l’Université de MontréalUniversité LavalUniversité de MontréalIzaak Walton Killam Health CentreCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalDalhousie UniversityInstitute for Clinical Evaluative SciencesMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsOntologyClinical PracticeNursing practiceContent analysisHealth psychologyClinical trialContent (measure theory)Behavioral medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Implementation strategies are essential for translating evidence into routine clinical practice. Their effectiveness depends on specifying and deploying behavior change techniques (BCTs): observable, irreducible components that target determinants of clinician behavior. The Behavior Change Technique Ontology (BCTO) standardizes the identification and labeling of BCTs, yet it has been applied only sparingly in implementation research to date. PURPOSE: To characterize the nature and extent of BCTs explicitly reported or retrospectively identified in implementation trials that targeted evidence-based nursing practice. METHODS: In this secondary analysis of a prior systematic review, we coded BCTs across 151 implementation trials with a manual derived from the 281-item BCTO. One to two coders per study applied coding rules in NVivo; disagreements were resolved by consensus. Feasibility indicators included coder certainty ("Definitely" vs "Probably" present) and the need for extra coding rules. RESULTS: Trials contained 907 BCT instances: 857 in intervention arms, 50 in controls. We identified 100 of the BCTO's 281 techniques (35.6%), spanning 17 of its 20 parent groups. Intervention arms featured a median of four BCT instances (IQR 3-7) and four unique BCTs (IQR 3-5). The five most common BCTs were Instruct how to perform behavior (n = 273), Arrange informational support (n = 127), Deliver informational support (n = 83), Demonstrate behavior (n = 62), and Practice behavior (n = 43). Only 37% of BCT instances were coded with high certainty, and 17 supplementary decision rules were required for consistent coding. CONCLUSIONS: Implementation strategies targeting nursing practice rely on instructional and informational BCTs, with limited use of goal-directed, feedback-intensive or context-altering techniques that could enhance impact. CLINICAL TRIAL INFORMATION: The Clinical Trials Registration PROSPERO CRD42019130446.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.899
GPT teacher head0.737
Teacher spread0.161 · 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