CONTRIBUTION OF DIGITAL EDUCATIONAL TECHNOLOGIES, DESIGNED FOR FATHERS, IN PROMOTING BREASTFEEDING: AN INTEGRATIVE REVIEW
Bibliographic record
Abstract
ABSTRACT Objective: to analyze the contributions of digital educational technologies, designed for fathers, in promoting breastfeeding. Method: a literature review, integrative type, carried out in December 2022, after electronic consultations in the CINAHL, Web of Science, EMBASE, MEDLINE, BDENF, IBECS and LILACS databases, without restriction of language and publication time. Article selection and information extraction were performed by peers independently. Results: sample of seven articles were published between 2017 and 2022 in Australia, Canada and Ethiopia. Digital educational technologies developed for fathers were mobile apps, eHealth resources, and text messaging. These technologies contributed to improve access to information, sharing of experiences, paternal self-efficacy to support breastfeeding, knowledge and attitude about infant feeding, consequently, improving breastfeeding rates. Conclusion: digital educational technologies on breastfeeding, designed for fathers, are poorly studied. However, they are fundamental to improve paternal support in promoting breastfeeding; therefore, more research is needed for the development of other digital educational technologies for this target audience.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".