Reflective and Automatic Processes in Health Care Professional Behaviour: a Dual Process Model Tested Across Multiple Behaviours
Bibliographic record
Abstract
Clinicians’ behaviours require deliberate decision-making in complex contexts and may involve both impulsive (automatic) and reflective (motivational and volitional) processes. The purpose of this study was to test a dual process model applied to clinician behaviours in their management of type 2 diabetes. The design used six nested prospective correlational studies. Questionnaires were sent to general practitioners and nurses in 99 UK primary care practices, measuring reflective (intention, action planning and coping planning) and impulsive (automaticity) predictors for six guideline-recommended behaviours: blood pressure prescribing (N = 335), prescribing for glycemic control (N = 288), providing diabetes-related education (N = 346), providing weight advice (N = 417), providing self-management advice (N = 332) and examining the feet (N = 218). Respondent retention was high. A dual process model was supported for prescribing behaviours, weight advice, and examining the feet. A sequential reflective process was supported for blood pressure prescribing, self-management and weight advice, and diabetes-related education. Reflective and impulsive processes predict behaviour. Quality improvement interventions should consider both reflective and impulsive approaches to behaviour change.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".