Integrating Quality Improvement and Continuing Professional Development
Bibliographic record
Abstract
PURPOSE: To explore the perspectives of leaders in psychiatry and continuing professional development (CPD) regarding the relationship, opportunities, and challenges in integrating quality improvement (QI) and CPD. METHOD: In 2013-2014, the authors interviewed 18 participants in Canada: 10 psychiatrists-in-chief, 6 CPD leaders in psychiatry, and 2 individuals with experience integrating these domains in psychiatry who were identified through snowball sampling. Questions were designed to identify participants' perspectives about the definition, relationship, and integration of QI and CPD in psychiatry. Interviews were recorded and transcribed. An iterative, inductive method was used to thematically analyze the transcripts. To ensure the rigor of the analysis, the authors performed member checking and sampling until theoretical saturation was achieved. RESULTS: Participants defined QI as a concept measured at the individual, hospital, and health care system levels and CPD as a concept measured predominantly at the individual and hospital levels. Four themes related to the relationship between QI and CPD were identified: challenges with QI training, adoption of QI into the mental health care system, implementation of QI in CPD, and practice improvement outcomes. Despite participants describing QI and CPD as mutually beneficial, they expressed uncertainty about the appropriateness of aligning these domains within a mental health care context because of the identified challenges. CONCLUSIONS: This study identified challenges with aligning QI and CPD in psychiatry and yielded a framework to inform future integration efforts. Further research is needed to determine the generalizability of this framework to other specialties and health care professions.
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".