How Indigenous mothers experience selecting and using early childhood development services to care for their infants
Bibliographic record
Abstract
Purpose: Promoting a child’s healthy growth and development in the first six years of life is critical to their later health and well-being. Indigenous infants experience poorer health outcomes than non-Indigenous infants, yet little is understood about how parents access and use health services to optimize their infants’ growth and development. Exploring the experiences of Indigenous mothers who select and use early childhood development (ECD) services provides important lessons into how best to promote their access and use of health services.Methods: This qualitative interpretive description study was guided by the Two-Eyed Seeing framework and included interviews with 19 Indigenous mothers of infants less than two years of age and 7 providers of ECD services.Results: Mainstream (public) and Indigenous-led health promotion programs both promoted the access and use of services while Indigenous-led programs further demonstrated an ability to provide culturally safe and trauma and violence-informed care.Conclusions: Providers of Indigenous-led services are best suited to deliver culturally safe care for Indigenous mothers and infants. Providers of mainstream services, however, supported by government policies and funding, can better meet the needs of Indigenous mothers and infants by providing cultural safe and trauma and violence-informed care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".