Supporting Indigenous Family Caregivers of Children with Life-Threatening and Life-Limiting Illness in One Canadian Province: Healthcare Providers’ Perspectives
Bibliographic record
Abstract
Background: Indigenous peoples in Canada endure lasting effects of colonialism including racism, marginalization, and social, political, and geographic inequities resulting in disproportionate rates of disease and inequitable healthcare. Indigenous infants and children in Canada experience a high incidence of birth complications and illnesses, and families are often left to navigate the care of their child with limited resources. Accordingly, improved, culturally responsive and safe healthcare is needed to enhance child outcomes and optimize family well-being. Purpose and Methods: A qualitative study was conducted by our research team including family members of Indigenous children with serious illnesses, a Cultural Advisor, researchers, and pediatric clinicians. In one component of the study, we explored pediatric healthcare providers’ (HCPs) experiences of caring for Indigenous families of children with serious illness. A purposive sample of 19 pediatric healthcare providers took part in semi-structured qualitative interviews or one focus group. The data were analyzed thematically to identify common experiences and priorities for improved supportive healthcare. Findings: Five themes were identified representing insights from pediatric HCPs, with a focus on barriers to effective healthcare at the interpersonal, institutional, and system levels for seriously ill Indigenous children and their families. Ideas for enhancing the social and cultural responsiveness and safety of supportive healthcare were identified. Implications and Conclusions: The findings offer valuable insights and strategies for HCPs to support holistic, comprehensive, and culturally safe and responsive healthcare.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".