Implementation processes and capacity-building needs in Ontario maternal-newborn care hospital settings: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Maternal-newborn care does not always align with the best available evidence. Applying implementation science to change initiatives can help move evidence-informed practices into clinical settings. However, it remains unknown to what extent current implementation practices in maternal-newborn care align with recommendations from implementation science, and how confident nurses, other health professionals, and leaders are completing steps in the implementation process. We aimed to understand Ontario maternal-newborn teams’ (1) approaches to implementing practice changes and the extent to which their implementation processes aligned with an implementation science planned-action framework; and (2) perceptions of importance and confidence completing implementation activities. Methods We conducted a cross-sectional survey between September–November 2023. Using purposive sampling, we invited Ontario maternal-newborn nurses, other healthcare professionals, and leaders who had experience participating in or leading implementation projects to complete an online questionnaire. The questionnaire was informed by an implementation science framework, which includes three core phases (identify issue; build solutions; implement, evaluate, sustain). The questions probed respondents’ perceptions of frequency of completion, importance, and confidence for each of the 28 implementation activities. We used descriptive statistics for the closed-ended questions and grouped the written responses into categories. Results We received 73 responses from 57 Ontario maternal-newborn hospitals, the majority being nurses in point-of-care and leadership roles. Nearly all respondents agreed that each of the 28 implementation activities were important. Respondents reported always completing a median of 8 out of 28 activities, with the number of activities completed declining from phase 1 through to 3. Most respondents indicated they were somewhat confident completing the implementation activities and agreed their teams would benefit from increasing their knowledge and skills to use an evidence-informed approach to implementing practice changes. Conclusions Despite viewing implementation activities as important, many teams are not consistently doing them and lack confidence, particularly in later phases of the implementation process. These findings inform where further capacity-building and supports may be needed to enable maternal-newborn nurses, other healthcare professionals, and leaders to apply implementation science to their change initiatives.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".