Healthcare professional behaviour: health impact, prevalence of evidence-based behaviours, correlates and interventions
Bibliographic record
Abstract
Healthcare professional (HCP) behaviours are actions performed by individuals and teams for varying and often complex patient needs. However, gaps exist between evidence-informed care behaviours and the care provided. Implementation science seeks to develop generalizable principles and approaches to investigate and address care gaps, supporting HCP behaviour change while building a cumulative science. We highlight theory-informed approaches for defining HCP behaviour and investigating the prevalence of evidence-based care and known correlates and interventions to change professional practice. Behavioural sciences can be applied to develop implementation strategies to support HCP behaviour change and provide valid, reliable tools to evaluate these strategies. There are thousands of different behaviours performed by different HCPs across many contexts, requiring different implementation approaches. HCP behaviours can include activities related to promoting health and preventing illness, assessing and diagnosing illnesses, providing treatments, managing health conditions, managing the healthcare system and building therapeutic alliances. The key challenge is optimising behaviour change interventions that address barriers to and enablers of recommended practice. HCP behaviours may be determined by, but not limited to, Knowledge, Social influences, Intention, Emotions and Goals. Understanding HCP behaviour change is a critical to ensuring advances in health psychology are applied to maximize population health.
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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 | 0.017 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".