Mapping behavior change techniques to characterize a social cognitive theory informed physical activity intervention for adults at risk of type 2 diabetes mellitus
Bibliographic record
Abstract
Behavior change techniques (BCTs) are used to target theoretical mechanisms of action predicted to bring about behavior change. Reporting BCTs and connecting them to mechanisms of action is critical to understanding intervention processes of change. This article identifies the BCTs associated with an exercise intervention for individuals at risk of type 2 diabetes and determines the extent to which these BCTs target associated mechanisms of action. BCTs were mapped onto social cognitive theory (SCT) and the theoretical domains framework (TDF) using published literature identifying links between BCTs and SCT/TDF and expert consensus. Two coders then used the 93-item BCT taxonomy (BCTTv1) to independently code BCTs within the intervention. The BCTs used in the current intervention enabled identification of the theoretical mechanisms of action targeted in the intervention. More than 70% of the intervention content incorporated at least one BCT. More than 50% of the BCTs used targeted SCT constructs and more than 70% of BCTs used targeted at least one of the 14 TDF domains. Five BCTs did not map onto either SCT or TDF. This research provides a systematic method of linking BCTs to mechanisms of action. This process increases the transparency of intervention content and identification of the mechanisms of action targeted in the current intervention. Reporting interventions in this manner will enable the most potent mechanisms of actions associated with long-term behavior change to be identified and utilized in future work. Trial Registration: ClinicalTrials.gov # NCT02164474. Registered on June 12, 2014.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".