A mixed methods evaluation of the maternal-newborn dashboard in Ontario: dashboard attributes, contextual factors, and facilitators and barriers to use: a study protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There are wide variations in maternal-newborn care practices and outcomes across Ontario. To help institutions and care providers learn about their own performance, the Better Outcomes Registry & Network (BORN) Ontario has implemented an audit and feedback system, the Maternal-Newborn Dashboard (MND), for all hospitals providing maternal-newborn care. The dashboard provides (1) near real-time feedback, with site-specific and peer comparison data about six key performance indicators; (2) a visual display of evidence-practice gaps related to the indicators; and (3) benchmarks to provide direction for practice change. This study aims to evaluate the effects of the dashboard, dashboard attributes, contextual factors, and facilitation/support needs that influence the use of this audit and feedback system to improve performance. The objectives of this study are to (1) evaluate the effect of implementing the dashboard across Ontario; (2) explore factors that potentially explain differences in the use of the MND among hospitals; (3) measure factors potentially associated with differential effectiveness of the MND; and (4) identify factors that predict differences in hospital performance. METHODS/DESIGN: A mixed methods design includes (1) an interrupted time series analysis to evaluate the effect of the intervention on six indicators, (2) key informant interviews with a purposeful sample of directors/managers from up to 20 maternal-newborn care hospitals to explore factors that influence the use of the dashboard, (3) a provincial survey of obstetrical directors/managers from all maternal-newborn hospitals in the province to measure factors that influence the use of the dashboard, and (4) a multivariable generalized linear mixed effects regression analysis of the indicators at each hospital to quantitatively evaluate the change in practice following implementation of the dashboard and to identify factors most predictive of use. DISCUSSION: Study results will provide essential data to develop knowledge translation strategies for facilitating practice change, which can be further evaluated through a future cluster randomized trial.
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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 | 0.028 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it