Building Capacity in Health Professionals to Conduct Quality Improvement
Bibliographic record
Abstract
BACKGROUND: The Toronto Academic Health Sciences Network Health Professions Innovation Fellowship Program began in 2014 as a pilot initiative among 4 academic teaching hospitals in Toronto, Ontario. The purpose of the Program was to cultivate applied leadership, interprofessional collaboration, and quality improvement capacity among health professionals. PURPOSE: This article reports on the evaluation findings from the initial year as well as an update on current program status and sustainability. METHODS: A formative evaluation was conducted focused on the impact on clinical practice, participant skill development, participant experience, and cross-organizational partnerships. Data were collected through a focus group, interviews, and pre- and postsurveys. RESULTS: Data from the initial pilot showed increases in leadership practices, project management, and quality improvement knowledge, with changes in leadership practices being significant. Positive changes in clinical practice at both the individual and unit/team levels and capacity for building relationships were also reported. Since the pilot, more than 160 participants from 15 health professions and 9 organizations have participated. Several graduates have taken on leadership roles since their participation in the Program. CONCLUSIONS: Health care organizations wishing to advance academic practice may benefit from implementing a similar collaborative program to reap benefits beyond organizational silos.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".