Strategies to increase physician engagement in acute care settings: a scoping review
Bibliographic record
Abstract
Purpose Despite growing attention to physician engagement there is a lack of literature to guide the development of physician-led interventions. A scoping review was conducted to describe physician-led strategies that have been implemented to promote increased physician engagement in acute care settings. Strategies are viewed through the theoretical lens of institutional work to advance the understanding about how the theory can be applied. The paper aims to discuss this issue. Design/methodology/approach Searches were conducted in English-language publications (2012–2017). Of 35 retained articles, 15 were from the gray literature; and 20 were peer reviewed. The review was guided by Arskey and O’Malley’s (2005) five-stage process. Findings Five themes reflecting different foci of physician-led activity were examined from the perspective of institutional work: systematically analyze context using participatory methods; work collaboratively toward locally defined, shared targets and build in processes to monitor progress; expand physicians’ role and capacity to include leadership toward shared organizational goals; promote appropriate rewards and incentives for work that builds engagement; and invest in opportunities for formal and informal communication and interaction. Practical implications Physicians considering action to increase their engagement in system improvement may benefit from analysis of local opportunities and barriers in selecting context-relevant activities that will motivate participation and build engagement through a balance of institutional work. Originality/value The paper considers the potential for physicians to initiate and support activity that will increase their engagement. It provides pragmatic strategies for designing intervention and research using the theoretical lens of institutional work.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".