Understanding Quality Improvement and Continuing Professional Mentorship: A Needs Assessment Study to Inform the Development of a Community of Practice
Bibliographic record
Abstract
INTRODUCTION: Quality improvement (QI) programming attempts to bridge the gap between patient care and standards of care. Mentorship could be a means through which QI is fostered, developed, and incorporated into continuing professional development (CPD) programs. The current study examined (1) models of implementation for mentorship within the Department of Psychiatry of a large Canadian academic center; (2) mentorship as a potential vehicle for alignment of QI practices and CPD; and (3) needs for the implementation of QI and CPD mentorship programs. METHODS: Qualitative interviews were conducted with 14 individuals associated with the university's Department of Psychiatry. The data were analyzed through thematic analyses with two independent coders using COREQ guidelines. RESULTS: Our results identified uncertainty among the participants regarding the conceptualization of QI and CPD, illustrating difficulties in determining whether mentorship could be used to align these practices. Three major themes were identified in our analyses: sharing of QI work through communities of practices; the need for organizational support; and relational experiences of QI mentoring. DISCUSSION: A greater understanding of QI is necessary before psychiatry departments can implement mentorship to enhance QI practices. However, models of mentorship and needs for mentorship have been made clear and include a good mentorship fit, organizational support, and opportunities for both formal and informal mentorship. Changing organizational culture and providing appropriate training is necessary for enhancing QI.
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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 | 0.064 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".