Quality improvement capacity: a survey of hospital quality managers
Bibliographic record
Abstract
Background Skilled managers are an important component of quality improvement (QI) infrastructure, but there has been little evaluation of QI infrastructure, which is needed to guide enhancement of this capacity. Methods Quality managers at 97 acute care hospitals in Ontario, Canada, were surveyed by mail to describe how their roles were integrated with QI performance objectives. Binary and scaled responses were analysed quantitatively, and open-ended responses were analysed thematically. Results The response rate was 79.4%. Many QI managers were new to their role and had no support staff despite responsibility for multiple portfolios. Respondents thought that QI objectives should be less reactive to hospital executives or boards, adverse events or demands from government and accreditation bodies, and recommended that dedicated QI managers proactively apply explicit strategic plans and engage executives and clinicians. Findings were consistent regardless of rank, staffing or hospital type. Those with master's training and greater experience were more involved in strategic planning, data analysis and communication. Conclusions QI is not well resourced in most acute care hospitals in Ontario. To develop QI capacity, investment and QI training may be required. Research should empirically establish objective performance measures of QI capacity to guide investment and evaluation.
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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 | 0.078 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".