Healthcare professional behaviour: health impact, prevalence of evidence-based behaviours, correlates and interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it