Bibliographic record
Abstract
BACKGROUND: Literature on health system transformation highlights the importance of physician engagement, suggesting that it is a critical factor for lowering costs while improving efficiency, quality of care, patient safety, physician satisfaction and retention. "Engagement" in health care is often defined as a positive, fulfilling work-related state of mind, which is characterized by vigor, dedication and absorption. The aim of this scoping review is to identify factors associated with, and tools used to measure physician engagement. METHODS: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and gray literature were searched. Supplementary articles were obtained by searching article references. All quantitative and qualitative study designs were eligible that described factors associated with, and tools used to measure, hospital physician engagement. Quantitative and qualitative analyses were conducted. Groupings and clustering were conducted to determine dominant groups or cluster of characteristics. Conceptual mapping was then conducted to identify patterns. RESULTS: A total of 15 studies fulfilled the eligibility criteria. All were published between 2012 and 2017. Studies were predominantly conducted in Germany (n=8). Factors associated with physician engagement were synthesized into individual characteristics (n=7), work environment characteristics (n=7), and work outcomes (n=5). The Utrecht Work Engagement Scale was the most commonly used tool (n=14). CONCLUSIONS: This scoping review provides a strong evidence-based platform to further advance knowledge in the area of physician engagement. The identification of environmental factors assists hospital administrative leaders in understanding how they might intervene to affect engagement, while the identification of individual characteristics enable identification of vulnerable physicians, permitting identification of the most pertinent targeted areas for focus.
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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".