Occurrence of and referral to specialists for pain-related diagnoses in First Nations and non–First Nations children and youth
Bibliographic record
Abstract
BACKGROUND: Indigenous youth have higher rates of chronic health conditions interfering with healthy development, including high rates of ear, dental, chest and musculoskeletal pain, as well as headache, arthritis and mental health issues. This study explores differences in pain-related diagnoses in First Nations and non–First Nations children. METHODS: Data from a study population of age- and sex-matched First Nations and non–First Nations children and youth were accessed from a specific region of Atlantic Canada. The primary objective of the study was to compare diagnosis rates of painful conditions and specialist visits between cohorts. The secondary objective was to determine whether there were correlations between early physical pain exposure and pain in adolescence (physical and mental health). RESULTS: Although ear- and throat-related diagnoses were more likely in the First Nations group than in the non–First Nations group (ear 67.3% v. 56.8%, p < 0.001; throat 89.3% v. 78.8%, p < 0.001, respectively), children in the First Nations group were less likely to see a relevant specialist (ear 11.8% v. 15.5%, p < 0.001; throat 12.7% v. 16.1%, p < 0.001, respectively). First Nations newborns were more likely to experience an admission to the neonatal intensive care unit (NICU) than non–First Nations newborns (24.4% v. 18.4%, p < 0.001, respectively). Non–First Nations newborns experiencing an NICU admission were more likely to receive a mental health diagnosis in adolescence, but the same was not found with the First Nations group (3.4% v. 5.7%, p < 0.03, respectively). First Nations children with a diagnosis of an ear or urinary tract infection in early childhood were almost twice as likely to have a diagnosis of headache or abdominal pain as adolescents (odds ratio [OR] 1.9, 95% confidence interval [CI] 1.1–3.0, and OR 1.7, 95% CI 1.2–2.3, respectively). INTERPRETATION: First Nations children were diagnosed with more pain than non–First Nations children, but did not access specific specialists or mental health services, and were not diagnosed with mental health conditions, at the same rate as their non–First Nations counterparts. Discrepancies in pain-related diagnoses and treatment are evident in these specific comparative cohorts. Community-based health care access and treatment inquiries are required to determine ways to improve care delivery for common childhood conditions that affect health and development.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".