Changing healthcare professionals' non-reflective processes to improve the quality of care
Bibliographic record
Abstract
RATIONALE: Translating research evidence into clinical practice to improve care involves healthcare professionals adopting new behaviours and changing or stopping their existing behaviours. However, changing healthcare professional behaviour can be difficult, particularly when it involves changing repetitive, ingrained ways of providing care. There is an increasing focus on understanding healthcare professional behaviour in terms of non-reflective processes, such as habits and routines, in addition to the more often studied deliberative processes. Theories of habit and routine provide two complementary lenses for understanding healthcare professional behaviour, although to date, each perspective has only been applied in isolation. OBJECTIVES: To combine theories of habit and routine to generate a broader understanding of healthcare professional behaviour and how it might be changed. METHODS: Sixteen experts met for a two-day multidisciplinary workshop on how to advance implementation science by developing greater understanding of non-reflective processes. RESULTS: From a psychological perspective 'habit' is understood as a process that maintains ingrained behaviour through a learned link between contextual cues and behaviours that have become associated with those cues. Theories of habit are useful for understanding the individual's role in developing and maintaining specific ways of working. Theories of routine add to this perspective by describing how clinical practices are formed, adapted, reinforced and discontinued in and through interactions with colleagues, systems and organisational procedures. We suggest a selection of theory-based strategies to advance understanding of healthcare professionals' habits and routines and how to change them. CONCLUSION: Combining theories of habit and routines has the potential to advance implementation science by providing a fuller understanding of the range of factors, operating at multiple levels of analysis, which can impact on the behaviours of healthcare professionals, and so quality of care provision.
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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 | 0.137 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".