Caring for Indigenous families in the neonatal intensive care unit
Bibliographic record
Abstract
Inequitable access to health care, social inequities, and racist and discriminatory care has resulted in the trend toward poorer health outcomes for Indigenous infants and their families when compared to non-Indigenous families in Canada. How Indigenous mothers experience care during an admission of their infant to the Neonatal Intensive Care Unit has implications for future health-seeking behaviors which may influence infant health outcomes. Nurses are well positioned to promote positive health care interactions and improve health outcomes by effectively meeting the needs of Indigenous families. This qualitative study was guided by interpretive description and the Two-Eyed Seeing framework and aimed to understand how Indigenous mothers experience accessing and using the health care system for their infants. Data were collected by way of interviews and a discussion group with self-identifying Indigenous mothers of infants less than two years of age living in Hamilton, Ontario, Canada. Data underwent thematic analysis, identifying nursing strategies to support positive health care interactions and promote the health and wellness of Indigenous infants and their families. Building relationships, providing holistic care, and taking a trauma-informed approach to the involvement of child protection services are three key strategies that nurses can use to positively impact health care experiences for Indigenous families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".