Sharing Empirical Knowledge to Improve Breastfeeding Promotion and Support: Description of a Research Dissemination Project
Bibliographic record
Abstract
BACKGROUND: Effective transfer of research findings to health care settings is a shared priority among researchers, clinicians, and decision makers. A multidisciplinary, multi-method investigation conducted in 2001 that explored breastfeeding practices and support within a large immigrant community in Montreal, Quebec, Canada, bore numerous implications for practice. Peer-reviewed funding was subsequently granted to support dissemination of these findings to relevant stakeholders. METHOD: Key steps in implementing this research dissemination project included (1) identifying and attracting target audiences from hospitals, community health settings, and government agencies; (2) tailoring tools for communication of research findings to the various needs of audiences; (3) designing interactive workshops to facilitate knowledge uptake; and (4) integrating the project outcomes within a government-sponsored regional breastfeeding committee for longer-term impact. FINDINGS: Despite organizational challenges, more than 90 health care providers, decision makers, and breastfeeding support volunteers participated in the project workshops. Through feedback loops, the dissemination activities contributed new layers of understanding to the original research findings. The activities also engaged audience members to identify more effective breastfeeding support interventions and led to the adoption of breastfeeding support priorities shared by hospital, community, and government stakeholders. CONCLUSION: This dissemination project provided unique opportunities for researchers and stakeholders to share in the interpretation of research findings and to strategically plan for future interventions to promote and support breastfeeding within ethnically diverse communities. Further research dissemination work should continue to be theoretically grounded, include systematic, long-term assessment of dissemination outcomes, and be adequately financed throughout.
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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 | 0.136 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".