The relationship between habit and healthcare professional behaviour in clinical practice: a systematic review and meta-analysis
Bibliographic record
Abstract
Theories of behaviour used to understand healthcare professional behaviour often focus on the deliberative processes that drive their behaviour; however, less is known about the role that implicit processes such as habit have on healthcare professional behaviour. This systematic review aimed to critically appraise and synthesise research evidence investigating the association between habit and healthcare professional behaviour. A search of five databases (PsycINFO, EMBASE, Scopus and CINAHL) was conducted up until 29 February 2016 to identify studies reporting correlations between habit and healthcare professional behaviours. Meta-analyses were conducted to assess the overall habit-behaviour association across all behaviours. A subgroup analysis assessed whether the habit-behaviour relationship differed depending on whether the behaviour was objectively measured or assessed by self-report. We identified nine eligible studies involving 1975 healthcare professionals that included 28 habit-behaviour correlations. A combined mean r+ of 0.35 (medium effect) was observed between habit and healthcare professional behaviour. The habit-behaviour correlation was not affected by whether behaviour was measured objectively or by self-report. This review suggests that habit plays a significant role in healthcare professional behaviour. Findings may have implications for considering health professionals’ habit when promoting the provision of evidence-based health care, and for breaking existing habit when de-implementing outdated, non-evidence-based practices.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".