Impact of remote prenatal education on program participation and breastfeeding of women in rural and remote Indigenous communities
Bibliographic record
Abstract
Background First Nations (FN) women have a higher risk of diabetes than non-FN women in Canada. Prenatal education and breastfeeding may reduce the risk of diabetes in mothers and offspring. The rates of breastfeeding initiation and participation in the prenatal program are low in FN communities. Methods A prenatal educational website, social media-assisted prenatal chat groups and community support teams were developed in three rural or remote FN communities in Manitoba. The rates of participation of pregnant women in prenatal programs and breastfeeding initiation were compared before and after the start of the remote prenatal education program within 2014-2017. Findings The participation rate of FN pregnant women in rural or remote communities in the prenatal program and breastfeeding initiation during 1-year after the start of the community-based remote prenatal education program were significantly increased compared to that during 1-year before the start of the program (54% versus 36% for the participation rate, 50% versus 34% for breastfeeding initiation, p < 0·001). Availability of high-speed Wi-Fi and/or postpartum supporting team were associated with favorite study outcomes. Positive feedback on the remote prenatal education was received from participants. Interpretation The findings suggest that remote prenatal education is feasible and effective for improving the breastfeeding rate and engaging pregnant women to participate in the prenatal program in rural or remote FN communities. The remote prenatal education remained active during COVID-19 in the participating communities, which suggests an advantage to expand remote prenatal education in other Indigenous communities. Funding Canadian Institutes of Health Research, the Lawson Foundation and University of Manitoba.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".