Bibliographic record
Abstract
The purpose of this article is to provide specific recommendations to enhance physician engagement in health care organizations. It summarizes the evidence on physician engagement, drawing on peer-reviewed articles and reports from the gray literature, and suggests an integrative framework to help health care managers better understand and improve physician engagement. While we examine some other international examples and experiences, we mainly focus on physician engagement in Canada, the United States, and the United Kingdom. Physician engagement can be conceptualized as an ongoing two-way social process in which both the individual and organizational/cultural components are considered. Building on several frameworks and examples, we propose a new integrative framework for enhancing physician engagement in health care organizations. We suggest that in order to enhance physician engagement, organizations should focus on the following strategies: developing clear and efficient communication channels with physicians; building trust, understanding, and respect with physicians; and identifying and developing physician leaders. We propose that the time is now for health care managers to set aside traditional differences and historical conflicts and to engage their physicians for the betterment of their organizations.
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.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".