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Record W1987192180 · doi:10.3399/bjgp12x659466

Making health habitual: the psychology of ‘habit-formation’ and general practice

2012· article· en· W1987192180 on OpenAlexaff
Benjamin Gardner, Phillippa Lally, Jane Wardle

Bibliographic record

VenueBritish Journal of General Practice · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsLondon Health Sciences Centre
FundersMedical Research CouncilCancer Research UK
KeywordsBehaviour changeAdvice (programming)Health careHealth professionalsBehavior changeMedicinePsychologyHabitApplied psychologySocial psychologyNursingComputer sciencePsychological intervention

Abstract

fetched live from OpenAlex

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 …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.031
Scholarly communication0.0050.006
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.125
GPT teacher head0.494
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations578
Published2012
Admission routes1
Has abstractyes

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