Protocol for establishing an infant feeding database linkable with population-based administrative data: a prospective cohort study in Manitoba, Canada
Bibliographic record
Abstract
INTRODUCTION: Breast feeding is associated with many health benefits for mothers and infants. But despite extensive public health efforts to promote breast feeding, many mothers do not achieve their own breastfeeding goals; and, inequities in breastfeeding rates persist between high and low-income mother-infant dyads. Developing targeted programme to support breastfeeding dyads and reduce inequities between mothers of different socioeconomic status are a priority for public health practitioners and health policy decision-makers; however, many jurisdictions lack the timely and comprehensive population-level data on infant-feeding practices required to monitor trends in breastfeeding initiation and duration. This protocol describes the establishment of a population-based infant-feeding database in the Canadian province of Manitoba, providing opportunities to develop and evaluate breastfeeding support programme. METHODS AND ANALYSIS: Routinely collected administrative health data on mothers' infant-feeding practices will be captured during regular vaccination visits using the Teleform fax tool, which converts handwritten information to an electronic format. The infant-feeding data will be linked to the Manitoba Population Research Data Repository, a comprehensive collection of population-based information spanning health, education and social services domains. The linkage will allow us to answer research questions about infant-feeding practices and to evaluate how effective current initiatives promoting breast feeding are. ETHICS AND DISSEMINATION: Approvals have been granted by the Health Research Ethics Board at the University of Manitoba. Our integrative knowledge translation approach will involve disseminating findings through government and community briefings, presenting at academic conferences and publishing in scientific journals.
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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 | 0.058 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".