Exploring the Connection between Physician Involvement in Quality Improvement and Medical Engagement
Bibliographic record
Abstract
Substantial efforts are required to improve the performance of healthcare systems, however, healthcare organizations tend to have structures and cultures that are highly resistant to change. Though such resistance will make significant changes in healthcare challenging there has been a call for reform in Canada’s healthcare system (e.g. Atkinson et al., 2011; CMA, 2012; Clark, 2012; Denis et al., 2013; Dickinson Ham, 2008; Dickson, 2011; Gosfield Reinertsen, 2007; Kirby, 2002; Romanow, 2002; Tuohy, 1999, 2002; Willis et al., 2012).Medical engagement has been suggested as one means of achieving this desired reform and overcoming the challenges of resistance to change (Baker Denis, 2011; Singer Shortell, 2011). Similarly, the involvement of physicians in quality improvement has been purported to contribute to improved health outcomes and decreased costs (Peterson, Jaen Phillips, 2013). Baker Denis (2011) note that many of the growing efforts to engage physicians in leading change are focused on changes in organizational structure and in broader system-wide leadership; however, there have been few studies examining the extent to which these changes have resulted in the enhanced levels of engagement. Similarly absent is empirical work examining the role of physician involvement in quality improvement in building medical engagement, despite suggestions that both will contribute to enhanced organizational and systems outcomes. The role that organizational commitment has on medical engagement is also of interest, given the recognized theoretical links between commitment and engagement. This thesis reports a mixed methods investigation to explore the connection between physician involvement in quality improvement, organizational commitment and medical engagement levels in two healthcare organizations in Ontario. In the first phase of the inquiry, organizational commitment and organizational support for quality improvement were quantified in a survey to determine the relationship among these concepts. The secondary, qualitative phase, allows for a deeper understanding of physicians’ perspectives regarding the connection between quality improvement, organizational commitment and medical engagement while exploring the results of the survey. Overall, this dissertation furthers our knowledge of how Canadian healthcare organizations can effectively work with physicians to drive changes to improve healthcare.
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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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".