Changing behaviour ‘more or less’—do theories of behaviour inform strategies for implementation and de-implementation? A critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Implementing evidence-based care requires healthcare practitioners to do less of some things (de-implementation) and more of others (implementation). Variations in effectiveness of behaviour change interventions may result from failure to consider a distinction between approaches by which behaviour increases and decreases in frequency. The distinction is not well represented in methods for designing interventions. This review aimed to identify whether there is a theoretical rationale to support this distinction. METHODS: Using Critical Interpretative Synthesis, this conceptual review included papers from a broad range of fields (biology, psychology, education, business) likely to report approaches for increasing or decreasing behaviour. Articles were identified from databases using search terms related to theory and behaviour change. Articles reporting changes in frequency of behaviour and explicit use of theory were included. Data extracted were direction of behaviour change, how theory was operationalised, and theory-based recommendations for behaviour change. Analyses of extracted data were conducted iteratively and involved inductive coding and critical exploration of ideas and purposive sampling of additional papers to explore theoretical concepts in greater detail. RESULTS: Critical analysis of 66 papers and their theoretical sources identified three key findings: (1) 9 of the 15 behavioural theories identified do not distinguish between implementation and de-implementation (5 theories were applied to only implementation or de-implementation, not both); (2) a common strategy for decreasing frequency was substituting one behaviour with another. No theoretical basis for this strategy was articulated, nor were methods proposed for selecting appropriate substitute behaviours; (3) Operant Learning Theory makes an explicit distinction between techniques for increasing and decreasing frequency. DISCUSSION: Behavioural theories provide little insight into the distinction between implementation and de-implementation. Operant Learning Theory identified different strategies for implementation and de-implementation, but these strategies may not be acceptable in health systems. Additionally, if behaviour substitution is an approach for de-implementation, further investigation may inform methods or rationale for selecting the substitute behaviour.
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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 | 0.401 | 0.606 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.039 | 0.021 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| 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".