MétaCan
Menu
Back to cohort
Record W4388855409 · doi:10.1186/s13012-023-01318-8

Identifying behaviour change techniques in 287 randomized controlled trials of audit and feedback interventions targeting practice change among healthcare professionals

2023· review· en· W4388855409 on OpenAlexaff
Jacob Crawshaw, Carly Meyer, Vivi Antonopoulou, Jesmin Antony, Jeremy Grimshaw, Noah Ivers, Kristin J. Konnyu, Meagan Lacroix, Justin Presseau, Michelle Simeoni, Sharlini Yogasingam, Fabiana Lorencatto

Bibliographic record

VenueImplementation Science · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa HospitalWomen's College HospitalMcMaster UniversityUniversity of OttawaHamilton Health Sciences
FundersNational Institute for Health and Care Research
KeywordsPsychological interventionMedicineAuditRandomized controlled trialHealth careBehaviour changeSystematic reviewBehavior changeBehavior change methodsMedical educationMEDLINENursing

Abstract

fetched live from OpenAlex

BACKGROUND: Audit and feedback (A&F) is among the most widely used implementation strategies, providing healthcare professionals with summaries of their practice performance to prompt behaviour change and optimize care. Wide variability in effectiveness of A&F has spurred efforts to explore why some A&F interventions are more effective than others. Unpacking the variability of the content of A&F interventions in terms of their component behaviours change techniques (BCTs) may help advance our understanding of how A&F works best. This study aimed to systematically specify BCTs in A&F interventions targeting healthcare professional practice change. METHODS: We conducted a directed content analysis of intervention descriptions in 287 randomized trials included in an ongoing Cochrane systematic review update of A&F interventions (searched up to June 2020). Three trained researchers identified and categorized BCTs in all trial arms (treatment & control/comparator) using the 93-item BCT Taxonomy version 1. The original BCT definitions and examples in the taxonomy were adapted to include A&F-specific decision rules and examples. Two additional BCTs ('Education (unspecified)' and 'Feedback (unspecified)') were added, such that 95 BCTs were considered for coding. RESULTS: In total, 47/95 BCTs (49%) were identified across 360 treatment arms at least once (median = 5.0, IQR = 2.3, range = 129 per arm). The most common BCTs were 'Feedback on behaviour' (present 89% of the time; e.g. feedback on drug prescribing), 'Instruction on how to perform the behaviour' (71%; e.g. issuing a clinical guideline), 'Social comparison' (52%; e.g. feedback on performance of peers), 'Credible source' (41%; e.g. endorsements from respected professional body), and 'Education (unspecified)' (31%; e.g. giving a lecture to staff). A total of 130/287 (45%) control/comparator arms contained at least one BCT (median = 2.0, IQR = 3.0, range = 0-15 per arm), of which the most common were identical to those identified in treatment arms. CONCLUSIONS: A&F interventions to improve healthcare professional practice include a moderate range of BCTs, focusing predominantly on providing behavioural feedback, sharing guidelines, peer comparison data, education, and leveraging credible sources. We encourage the use of our A&F-specific list of BCTs to improve knowledge of what is being delivered in A&F interventions. Our study provides a basis for exploring which BCTs are associated with intervention effectiveness. TRIAL REGISTRATIONS: N/A.

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)0.281
metaresearch head score (Gemma)0.599
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.886

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2810.599
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0080.015
Bibliometrics0.0140.015
Science and technology studies0.0020.004
Scholarly communication0.0060.005
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.894
GPT teacher head0.788
Teacher spread0.106 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreReview

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

Citations42
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueImplementation ScienceSame topicHealth Policy Implementation ScienceFrench-language works237,207