A mixed-methods evaluation of the MOREOB program in Ontario hospitals: participant knowledge, organizational culture, and experiences
Bibliographic record
Abstract
MORE OB (Managing Obstetrical Risk Efficiently) is a patient safety program for health care providers and administrators in hospital obstetric units. MORE OB has been implemented widely in Canada and gradually spread to the United States. The main goal of MORE OB is to build a patient safety culture and improve clinical outcomes. In 2013, 26 Ontario hospitals voluntarily accepted provincial funding to participate in MORE OB . The purpose of our study was to assess the effect of MORE OB on participant knowledge, organizational culture, and experiences implementing and participating in the program at these 26 Ontario hospitals. A convergent parallel mixed-methods study in Ontario, Canada, with MORE OB participants from 26 hospitals. The quantitative component used a descriptive pre-post repeated measures design to assess participant knowledge and perception of culture, administered pre-MORE OB and after each of the three MORE OB modules. Changes in mean scores were assessed using mixed-effects regression. The qualitative component used a qualitative descriptive design with individual semi-structured interviews. We used content analysis to code, categorize, and thematically describe data. A convergent parallel design was used to triangulate findings from data sources. 308 participants completed the knowledge test, and 329 completed the culture assessment at all four time points. Between baseline and post-Module 3, statistically significant increases on both scores were observed, with an increase of 7.9% (95% CI: 7.1 to 8.8) on the knowledge test and an increase of 0.45 (on a scale of 1–5, 95% CI: 0.38 to 0.52) on the culture assessment. Interview participants ( n = 15) described improvements in knowledge, interprofessional communication, ability to provide safe care, and confidence in skills. Facilitators and barriers to program implementation and sustainability were identified. Participants were satisfied with their participation in the MORE OB program and perceived that it increased health care provider knowledge and confidence, improved safety for patients, and improved communication between team members. Additionally, mean scores on knowledge tests for obstetric content and culture assessment improved. The MORE OB program can help organizations and individuals improve care by concentrating on the human and organizational aspects of patient safety. Further work to improve program implementation and sustainability is required.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".