Planning and implementing practice changes in Ontario maternal-newborn hospital units: a secondary qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Moving evidence into practice is complex, and pregnant and birthing people and their infants do not always receive care that aligns with the best available evidence. Implementation science can inform how to effectively move evidence into practice. While there are a growing number of examples of implementation science being studied in maternal-newborn care settings, it remains unknown how real-world teams of healthcare providers and leaders approach the overall implementation process when making practice changes. The purpose of this study was to describe maternal-newborn hospital teams' approaches to implementing practice changes. We aimed to identify what implementation steps teams take (or not) and identify strengths and potential areas for improvement based on best practices in implementation science. METHODS: We conducted a supplementary qualitative secondary analysis of 22 interviews completed in 2014-2015 with maternal-newborn nursing leaders in Ontario, Canada. We used directed content analysis to code the data to seven steps in an implementation framework (Implementation Roadmap): identify the problem and potential best practice; assemble local evidence; select and customize best practice; discover barriers and drivers; tailor implementation strategies; field-test, plan evaluation, prepare to launch; launch, evaluate, and sustain. Frequency counts are presented for each step. RESULTS: Participants reported completing a median of 4.5 of 7 Implementation Roadmap steps (range = 3-7), with the most common being identifying a practice problem. Other steps were described less frequently (e.g., selecting and adapting evidence, field-testing, outcome evaluation) or discussed frequently but not optimally (e.g., barriers assessment). Participants provided examples of how they engaged point-of-care staff throughout the implementation process, but provided fewer examples of engaging pregnant and birthing people and their families. Some participants stated they used a formal framework or process to guide their implementation process, with the most common being quality improvement approaches and tools. CONCLUSIONS: We identified variability across the 22 hospitals in the implementation steps taken. While we observed many strengths, we also identified areas where further support may be needed. Future work is needed to create opportunities and resources to support maternal-newborn healthcare providers and leaders to apply principles and tools from implementation science to their practice 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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".