Using a behaviour change techniques taxonomy to identify active ingredients within trials of implementation interventions for diabetes care
Bibliographic record
Abstract
BACKGROUND: Methodological guidelines for intervention reporting emphasise describing intervention content in detail. Despite this, systematic reviews of quality improvement (QI) implementation interventions continue to be limited by a lack of clarity and detail regarding the intervention content being evaluated. We aimed to apply the recently developed Behaviour Change Techniques Taxonomy version 1 (BCTTv1) to trials of implementation interventions for managing diabetes to assess the capacity and utility of this taxonomy for characterising active ingredients. METHODS: Three psychologists independently coded a random sample of 23 trials of healthcare system, provider- and/or patient-focused implementation interventions from a systematic review that included 142 such studies. Intervention content was coded using the BCTTv1, which describes 93 behaviour change techniques (BCTs) grouped within 16 categories. We supplemented the generic coding instructions within the BCTTv1 with decision rules and examples from this literature. RESULTS: Less than a quarter of possible BCTs within the BCTTv1 were identified. For implementation interventions targeting providers, the most commonly identified BCTs included the following: adding objects to the environment, prompts/cues, instruction on how to perform the behaviour, credible source, goal setting (outcome), feedback on outcome of behaviour, and social support (practical). For implementation interventions also targeting patients, the most commonly identified BCTs included the following: prompts/cues, instruction on how to perform the behaviour, information about health consequences, restructuring the social environment, adding objects to the environment, social support (practical), and goal setting (behaviour). The BCTTv1 mapped well onto implementation interventions directly targeting clinicians and patients and could also be used to examine the impact of system-level interventions on clinician and patient behaviour. CONCLUSIONS: The BCTTv1 can be used to characterise the active ingredients in trials of implementation interventions and provides specificity of content beyond what is given by broader intervention labels. Identification of BCTs may provide a more helpful means of accumulating knowledge on the content used in trials of implementation interventions, which may help to better inform replication efforts. In addition, prospective use of a behaviour change techniques taxonomy for developing and reporting intervention content would further aid in building a cumulative science of effective implementation 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 | 0.127 | 0.234 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".