Evidence‐based selection of theories for designing behaviour change interventions: Using methods based on theoretical construct domains to understand clinicians' blood transfusion behaviour
Bibliographic record
Abstract
BACKGROUND: Many theories of behaviour are potentially relevant to predictive and intervention studies but most studies investigate a narrow range of theories. Michie et al. (2005) agreed 12 'theoretical domains' from 33 theories that explain behaviour change. They developed a 'Theoretical Domains Interview' (TDI) for identifying relevant domains for specific clinical behaviours, but the framework has not been used for selecting theories for predictive studies. It was used here to investigate clinicians' transfusion behaviour in intensive care units (ICU). Evidence suggests that red blood cells transfusion could be reduced for some patients without reducing quality of care. OBJECTIVES: (1) To identify the domains relevant to transfusion practice in ICUs and neonatal intensive care units (NICUs), using the TDI. (2) To use the identified domains to select appropriate theories for a study predicting transfusion behaviour. METHODS: An adapted TDI about managing a patient with borderline haemoglobin by watching and waiting instead of transfusing red blood cells was used to conduct semi-structured, one-to-one interviews with 18 intensive care consultants and neonatologists across the UK. RESULTS: Relevant theoretical domains were: knowledge, beliefs about capabilities, beliefs about consequences, social influences, behavioural regulation. Further analysis at the construct level resulted in selection of seven theoretical approaches relevant to this context: Knowledge-Attitude-Behaviour Model, Theory of Planned Behaviour, Social Cognitive Theory, Operant Learning Theory, Control Theory, Normative Model of Work Team Effectiveness and Action Planning Approaches. CONCLUSIONS: This study illustrated, the use of the TDI to identify relevant domains in a complex area of inpatient care. This approach is potentially valuable for selecting theories relevant to predictive studies and resulted in greater breadth of potential explanations than would be achieved if a single theoretical model had been adopted.
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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 | 0.169 | 0.273 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".