Physician engagement: a concept analysis
Bibliographic record
Abstract
Tyrone A Perreira,1,2 Laure Perrier,3 Melissa Prokopy,2 Lina Neves-Mera,2 D David Persaud41Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; 2Legal, Policy and Professional Issues, Ontario Hospital Association, Toronto, Ontario, Canada; 3University of Toronto Libraries, University of Toronto, Toronto, Ontario, Canada; 4School of Health Administration at Dalhousie University, Dalhousie University, Halifax, Nova Scotia, CanadaAbstract: The term “physician engagement” is used quite frequently, yet it remains poorly defined and measured. The aim of this study is to clarify the term “physician engagement.” This study used an eight step-method for conducting concept analyses created by Walker and Avant. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched on February 14, 2019. No limitations were put on the searches with regard to year or language. Results identify that the term “physician engagement” is regular participation of physicians in (1) deciding how their work is done, (2) making suggestions for improvement, (3) goal setting, (4) planning, and (5) monitoring of their performance in activities targeted at the micro (patient), meso (organization), and/or macro (health system) levels. The antecedents of “physician engagement” include accountability, communication, incentives, interpersonal relations, and opportunity. The results include improved outcomes such as data quality, efficiency, innovation, job satisfaction, patient satisfaction, and performance. Defining physician engagement enables physicians and health care administrators to better appreciate and more accurately measure engagement and understand how to better engage physicians.Keywords: physician, medical, engagement, concept analysis
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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 | 0.078 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".