The Influence of Sociocultural Determinants on the Number of Diagnosed Chronic Illness Reported by Indigenous Peoples in Canada and the United States During SARS-CoV2
Bibliographic record
Abstract
Objectives: To determine the influence of cultural, including land-based, factors on the reported number of diagnosed chronic illnesses among Indigenous individuals living in Canada and the United States during SARS-CoV-2 (COVID-19). Methods: 557 Indigenous individuals completed the Hearing Indigenous Voices survey (HIS) in 2021. Data from HIS respondents living with at least one chronic illness was used to conduct a Poisson regression. This equation estimated the effect of change in ancestral land use, participation in cultural activities, and demographic characteristics on the number of diagnosed chronic illnesses reported by Indigenous individuals. Results: Results demonstrate that the number of chronic illnesses reported by Indigenous individuals during COVID-19 was positively correlated with 2 cultural factors. The first is spending a different amount of time on ancestral territories compared to before the pandemic ( P < .01). Participating in beading, traditional arts and crafts, or Indigenous storytelling ( P < .001) is the second. However, this relationship was moderated by age ( P < .01) and socio-economic status ( P < .001), with positive and negative associations respectively found for each demographic factor. Discussion: Cultural practices, including accessing ancestral territories, often positively contribute to Indigenous Peoples’ health. The increased number of diagnosed chronic illnesses among respondents who participate in cultural activities suggests that those living with chronic illness may not gain the same benefits from culture during pandemics because of the multitude of barriers they face during emergencies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".