Making health habitual: the psychology of ‘habit-formation’ and general practice
Bibliographic record
Abstract
The Secretary of State recently proposed that the NHS: ‘... take every opportunity to prevent poor health and promote healthy living by making the most of healthcare professionals’ contact with individual patients.’ 1 Patients trust health professionals as a source of advice on ‘lifestyle’ (that is, behaviour) change, and brief opportunistic advice can be effective.2 However, many health professionals shy away from giving advice on modifying behaviour because they find traditional behaviour change strategies time-consuming to explain and difficult for the patient to implement.2 Furthermore, even when patients successfully initiate the recommended changes, the gains are often transient3 because few of the traditional behaviour change strategies have built-in mechanisms for maintenance. Brief advice is usually based on advising patients on what to change and why (for example, reducing saturated fat intake to reduce the risk of heart attack). Psychologically, such advice is designed to engage conscious deliberative motivational processes, which Kahneman terms ‘slow’ or ‘System 2’ processes.4 However, the effects are typically short-lived because motivation and attention wane. Brief advice on how to change, engaging automatic (‘System 1’) processes, may offer a valuable alternative with potential for long-term impact. Opportunistic health behaviour advice must be easy for health professionals to give and easy for patients to implement to fit into routine health care. We propose that simple advice on how to make healthy actions into habits — externally-triggered automatic responses to frequently encountered contexts — offers a useful option in the behaviour change toolkit. Advice for creating habits is easy for clinicians to deliver and easy for patients to implement: repeat a chosen behaviour in the same context, until it becomes automatic and effortless. While often used as a synonym for frequent or customary behaviour in everyday parlance, within psychology, ‘habits’ are defined as actions that …
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".