Factors that impact access to ongoing health care for First Nation children with a chronic condition
Bibliographic record
Abstract
BACKGROUND: Access to multidisciplinary health care services for First Nation children with a chronic condition is critical for the child's health and well-being, but disparities and inequality in health care systems have been almost impossible to eradicate for First Nation people globally. The objective of this review is to identify the factors that impact access and ongoing care for First Nation children globally with a chronic condition. METHODS: An extensive systematic search was conducted of nine electronic databases to identify primary studies that explored factors affecting access to ongoing services for First Nation children with a chronic disease or injury. Due to the heterogeneity of included studies the Mixed Method Appraisal Tool (MMAT) was used to assess study quality. RESULTS: A total of six studies from Australia, New Zealand and Canada were identified and included in this review. Four studies applied qualitative approaches using in-depth semi structured interviews, focus groups and community fora. Two of the six studies used quantitative approaches. Facilitators included the utilisation of First Nation liaison workers or First Nation Health workers. Key barriers that emerged included lack of culturally appropriate health care, distance, language and cultural barriers, racism, the lack of incorporation of First Nation workers in services, financial difficulties and transport issues. CONCLUSION: There are few studies that have identified positive factors that facilitate access to health care for First Nation children. There is an urgent need to develop programs and processes to facilitate access to appropriate health care that are inclusive of the cultural needs of First Nation children.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".