Assessment of Quality Improvement in Ontario Public Health Units
Bibliographic record
Abstract
Background: Quality Improvement (QI) approaches are used extensively in healthcare settings and increasingly in public health. However, the proliferation of QI in Canadian public health settings is unknown. Purpose: The purpose of this study was to (a) assess the QI maturity in Ontario local public health units in Canada, and (b) to determine the relevance of the QI Maturity Tool in a Canadian setting Methods: The QI Maturity Tool (Version 5) was used to conduct a cross-sectional assessment of the QI maturity of 36 local public health units in Ontario, Canada. After tool items were reviewed for relevance, individuals most responsible for QI at each health unit were surveyed. Descriptive statistics were used to analyze the data. Results: Thirty-one individuals responded (response rate: 86%). Respondents reported strong leadership support for QI, but limited training and resources available to advance this area. Approximately half of public health units were found to be at the ‘beginner’ stage of QI maturity; 19% and 26% were in the ‘emerging’ and ‘progressive’ stages, respectively. Only 3% were in the ‘achieving’ stage and none are in the ‘excelling’ stage. Implications: The QI Maturity Tool is valuable for determining the maturity of QI in Ontario public health settings. There appears to be strong support for advancing QI across local public health in Ontario, but limited infrastructure to enable associated QI activities.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".