Planning to be routine: habit as a mediator of the planning-behaviour relationship in healthcare professionals
Bibliographic record
Abstract
BACKGROUND: Gaps in the quality of care provided to people with type 2 diabetes are regularly identified. Healthcare professionals often have a strong intention to follow practice guidelines during consultations with people with type 2 diabetes; however, this intention does not always translate into action. Action planning (planning when, where and how to act) and coping planning (planning how to overcome pre-identified barriers) have been hypothesised to help with the enactment of intentions by creating mental cue-response links that promote habit formation. This study aimed to investigate whether habit helps to better understand how action and coping planning relate to clinical behaviour in the context of type 2 diabetes care. METHODS: The study utilised a prospective correlational design with six nested sub-studies. General practitioners and practice nurses (n = 427 from 99 UK primary care practices) completed measures of action planning, coping planning and habit at baseline and then self-reported their enactment of guideline-recommended advising, prescribing and examining behaviours 12 months later. Bootstrapped mediation analyses were used to test the indirect effect of action and coping planning on healthcare professionals' clinical behaviour via their relationship with habit. RESULTS: Healthcare professionals who reported higher degrees of action or coping planning for performing six guideline recommended behaviours in the context of type 2 diabetes care were more likely to report performing these behaviours in clinical practice. All 12 bootstrapped mediation analyses showed that the positive relationship between planning (action and coping planning) and healthcare professionals' clinical behaviour operated indirectly through habit. CONCLUSIONS: These findings suggest that habit mediates the relationship between planning (action and coping planning) and healthcare professional behaviour. Promoting careful action and coping planning may support routinised uptake of guideline-recommended care by healthcare professionals in the primary care setting. Given the competing demands on healthcare professionals, exploring the behavioural processes involved in promoting more routinisation of behaviours where possible and appropriate could free up cognitive capacity for clinical behaviours that rely on more deliberation.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".