Behavior Change Techniques in Continuing Professional Development
Bibliographic record
Abstract
Continuing professional development (CPD) is a widely used and evolving set of complex interventions that seeks to update and improve the knowledge, skills, and performance of health care professionals to ultimately improve patient care and outcomes. While synthesized evidence shows CPD in general to be effective, effects vary, in part due to variation in CPD interventions and limited understanding of CPD mechanisms of action. We introduce two behavioral science tools-the Behavior Change Technique Taxonomy version 1 and the Theoretical Domains Framework-that can be used to characterize the content of CPD interventions and the determinants of behaviour potentially targeted by the interventions, respectively. We provide a worked example of the use of these tools in coding the educational content of 43 diabetes quality improvement trials containing clinician education as part of their multicomponent intervention. Fourteen (of a possible 93; 15%) behavior change techniques were identified in the clinician education content of the quality improvement trials, suggesting a focus of addressing the behavioral determinants beliefs about consequences, knowledge, skills, and social influences, of diabetes care providers' behavior. We believe that the Behavior Change Technique Taxonomy version 1 and Theoretical Domains Framework offer a novel lens to analyze the CPD content of existing evidence and inform the design and evaluation of future CPD interventions.
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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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".