Exploring Women’s Perspectives on Receiving AI-enabled Digital Support for Infant Feeding: a multi-methods cross-sectional study. (Preprint)
Bibliographic record
Abstract
BACKGROUND: Infant feeding practices, including breastfeeding, are known to benefit maternal and child health outcomes. Therefore, parent access to evidence-based infant feeding advice is critical. In recent years, there has been increased use of digital health technologies to support infant feeding. Despite its potential, using AI to complement existing health care and connect families to timely infant feeding support remains relatively unexplored. OBJECTIVE: This study aims to explore women's perceptions of using AI-enabled infant feeding support within mobile health (mHealth) interventions. The study investigates (A) openness to AI-enabled support, (B) experiences with existing AI-enabled support, (C) preferences for SMS text messages generated by AI versus "child and family health" nurses, and (D) opinions on infant feeding topics suitable for AI. METHODS: Two data collection activities were undertaken with women (primary caregivers) of infants aged 6-14 months, residing in the Hunter New England Local Health District (HNELHD) of New South Wales, Australia. Different women who received antenatal care in HNELHD were recruited for quantitative and qualitative data collection. Quantitative surveys assessed women's openness to receiving AI-enabled support (objective A). Descriptive and logistic regression analyses were conducted to explore associations between participant characteristics and openness to AI. Qualitative data collection involved focus groups to explore women's perceptions and preferences on infant feeding topics suitable for AI (objectives B, C, and D). Thematic analysis was used to analyze focus group transcripts. RESULTS: A total of 164 women completed the quantitative survey. Approximately 53% (87/164) of participants were open to receiving AI recommendations to see a health professional for infant feeding support, 34% (56/164) were open to AI assessing their breastfeeding experiences, and 41% (67/163) were open to AI providing advice to prevent or address breastfeeding challenges. Fewer Aboriginal and Torres Strait Islander participants were open to receiving AI-generated support (adjusted odds ratio 0.41, 95% CI 0.19-0.92) or advice to see a health professional (adjusted odds ratio 0.29, 95% CI 0.13-0.64). Twelve women participated in 3 online focus groups. Thematic analysis resulted in three overarching themes: (1) opportunities to fill gaps in support, (2) variable confidence engaging with AI for information and advice, and (3) potential convenience of AI and mHealth to offer timely support. CONCLUSIONS: The study highlights the potential of AI and barriers to women's acceptability and engagement. While women recognized the potential for AI to fill health care gaps in infant feeding support, including after business hours, there was less interest in AI replacing "in-person" support or information easily located via online search. Women's concerns regarding the credibility and trustworthiness of AI-enabled support should be addressed to maximize their use of emerging AI-enabled tools, embedded within digital technologies and mHealth. There is potential for AI to complement rather than replace usual care.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".