Indigenous Mothers’ Use of Web- and App-Based Information Sources to Support Healthy Parenting and Infant Health in Canada: Interpretive Description
Bibliographic record
Abstract
BACKGROUND: Web-based sources of health information are widely used by parents to support healthy parenting and aid in decision making about their infants' health. Although fraught with challenges such as misinformation, if used appropriately, web-based resources can improve access to health education and promote healthy choices. How Indigenous mothers use web-based information to support their parenting and infants' health has not yet been investigated; however, web-based modalities may be important methods for mitigating the reduced access to health care and negative health care interactions that many Indigenous people are known to experience. OBJECTIVE: This study aims to understand the experience of Indigenous mothers who use web-based information to support the health of their infants. METHODS: This interpretive description qualitative study used semistructured interviews and a discussion group to understand how Indigenous mothers living in Hamilton, Ontario and caring for an infant aged <2 years experienced meeting the health needs of their infants. The data presented reflect their experiences of using web-based sources of health information to support their infants' health. The Two-Eyed Seeing approach was applied to the study design, which ensured that both western and Indigenous worldviews were considered throughout. RESULTS: A total of 19 Indigenous mothers participated in this study. The resulting 4 themes included distrusting information, staying anonymous, using visual information to support decision making, and accessing a world of experiences. Although fewer Indigenous mothers used web-based sources of information compared to mothers in the general population in other studies, tailoring web-based modalities to meet the unique needs of Indigenous mothers is an important opportunity for supporting the health and wellness of both mothers and infants. CONCLUSIONS: Web-based information sources are commonly used among parents, and ever-evolving web-based technologies make this information increasingly available and accessible. Tailoring web-based modalities to meet the unique preferences and needs of Indigenous mothers is an important method for improving their access to reliable and accurate health care information, thereby supporting healthy parenting and promoting infant health.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".