User evaluation of an implementation toolkit for maternal–newborn care settings
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Applying knowledge generated from implementation science can improve the process teams use to move evidence into practice. To facilitate the use of implementation science knowledge in practice, training and resources for health care providers and leaders are needed. Our team developed a toolkit for maternal-newborn settings to provide guidance and tools on how to apply implementation science knowledge to their practice change initiatives. The aim of this study was to evaluate the toolkit for acceptability, appropriateness, feasibility, usability, and potential adoption. METHODS: We conducted a convergent mixed-methods study. Health care providers and leaders working in Ontario maternal-newborn care read the toolkit and completed a feedback survey and interview. The questionnaire included questions on toolkit content and format and validated measures on acceptability, appropriateness, feasibility, and usability. The interviews explored questionnaire responses and application of the toolkit. Quantitative data were analyzed descriptively. Qualitative data were analyzed using directed content analysis. RESULTS: Participants (n = 17) rated the toolkit as acceptable, appropriate, feasible, and usable, indicating the toolkit met the need for practical resources to guide implementation. Most participants indicated their intention to use the toolkit and refer it to others. Several areas for improvement were identified, including simplifying language and content and improving the format. Participants highlighted the need to effectively disseminate the toolkit and offer training and support for its successful implementation. CONCLUSIONS: The toolkit has the potential to improve maternal-newborn teams' implementation processes and outcomes. Future work is needed to improve the toolkit and evaluate its use and impact in practice. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A431.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".