Towards a taxonomy of behavior change techniques for promoting shared decision making
Bibliographic record
Abstract
BACKGROUND: There is little information about the functions and behavior change techniques (BCTs) needed to implement shared decision making (SDM) in clinical practice. To guide future implementation initiatives, we sought to develop a BCT taxonomy for SDM implementation interventions. METHODS: This study is a secondary analysis of a 2018 Cochrane review on interventions for increasing the use of shared decision making by healthcare professionals. We examined all 87 studies included in the review. We extracted relevant information on each study intervention into a spreadsheet. Coders had undergone a training workshop on intervention functions and online training on BCT Taxonomy version 1 (BCTTv1). We performed functions and BCTs coding trials, and identified coding rules. We used Michie's guide for designing behavior change interventions to code the functions and BCTs used in the interventions. Coders met to compare coding and discrepancies were discussed until consensus was reached. Data was analyzed using simple descriptive statistics. RESULTS: Overall, 7 functions, 24 combinations of functions and 32 BCTs were used in the 87 SDM implementation interventions. The mean of functions per intervention was 2.5 and the mean of BCTs per intervention was 3.7. The functions Coercion and Restriction were not found. The most common function was Education (73 studies). Three combinations of functions were most common (e.g: Education + Persuasion, used in 10 studies). The functions associated with more effective SDM implementation interventions were Modeling and Training. The most effective combination of functions was Education + Training + Modeling + Enablement. The most commonly used BCT was Instruction on how to perform the behavior (43 studies). BCTs associated with more effective SDM implementation interventions were: Instruction on how to perform the behavior, Demonstration of the behavior, Feedback on behavior, Pharmacological support, Material reward, and Biofeedback. Twenty-five BCTs were associated with less effective SDM implementation interventions. Four new BCTs were identified: General information to support the behavior, Tailoring, Exercises to conceptually prepare for the behavior, and Experience sharing and learning. CONCLUSIONS: We established a BCT taxonomy specific to the field of SDM to guide future SDM implementation interventions. Four new BCTs should be added to BCTTv1.
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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 | 0.093 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".