Study protocol for a multi-site case study evaluation of a Canadian quality improvement collaborative to improve Baby-Friendly practices in community health services
Bibliographic record
Abstract
INTRODUCTION: In Canada, many families want to breastfeed, but there are several common challenges they may encounter. Currently, 91% of Canadian families initiate breastfeeding after giving birth, yet only 38% of babies are breastfed exclusively to 6 months. In 1991, the Breastfeeding Committee for Canada (BCC) was established to implement the World Health Organization's Ten-Step Baby-Friendly Hospital Initiative, a series of evidence-based in-hospital practices to support families to breastfeed. Then, in recognition of the need to support breastfeeding beyond the hospital setting, the BCC expanded the Baby-Friendly Initiative (BFI) to apply the Ten Steps to both hospitals and community health settings. However, uptake of the BFI Ten Steps in community settings has been low and methodology on how to optimise implementation of the Ten Steps in community is not well developed. Therefore, the objective of this project is to develop and evaluate a quality improvement collaborative with 25 community health services from across Canada to learn how to best support the implementation of the BFI Ten Steps in community, with the ultimate goal of improving breastfeeding outcomes. METHODS AND ANALYSIS: This protocol describes the activities of the Community Baby-Friendly Initiative Collaborative (CBFI-C) and the methods used to evaluate its effectiveness. We will use the Institute for Healthcare Information Breakthrough Series (IHI-BTS) model, a proven quality improvement model that has been widely used in clinical settings, but is not yet widely used in community settings. The IHI-BTS combines three virtual learning sessions with action cycles that allow the participating sites time to test and track small practice changes. Sites will be asked to track care indicator and breastfeeding outcome data, engage in monthly webinars, receive coaching from trained mentors, participate in focus groups and participate in a final summative workshop. We will use a multi-site case study approach, combining aggregate care indicator data and qualitative data from webinars, focus groups and workshops to evaluate how the CBFI-C model supports community sites in the process of implementing the BFI Ten Steps. ETHICS AND DISSEMINATION: Ethics approval for this evaluation was obtained from the CHIPER Health Research Ethics Board (Number HS26947-H2025:157)). The results of the CBFI-C evaluation will be shared in a report, peer-reviewed publications and presentations to government and academic audiences. The findings will inform effective quality improvement strategies to enhance uptake of the BFI in community health settings.
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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 | 0.120 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.009 |
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".