A Scoping Review of the Current Knowledge of the Social Determinants of Health and Infectious Diseases (Specifically COVID-19, Tuberculosis, and H1N1 Influenza) in Canadian Arctic Indigenous Communities
Bibliographic record
Abstract
Social determinants of health (SDHs) and the impact of colonization can make Canadian Arctic Indigenous communities susceptible to infectious diseases, including the coronavirus disease 2019 (COVID-19). This scoping review followed the PRISMA guidelines for scoping reviews and studied what is known about selected pandemics (COVID-19, tuberculosis, and H1N1 influenza) and SDHs (healthcare accessibility, food insecurity, mental health, cultural continuity, housing, community infrastructure, and socioeconomic status (SES)) for Canadian Arctic Indigenous communities. Original studies published in English and French up to October 2024 were located in databases (PubMed, Medline, and CINAHL), AlterNative: An International Journal of Indigenous Peoples, and through reference tracking. We included 118 studies: 6 relating to COVID-19, 5 to influenza, 5 to TB, 27 to food insecurity, 26 to healthcare access, 22 to mental health, 9 to SES, 8 to housing, 7 to cultural continuity, and 3 to community infrastructure. SDHs affecting Indigenous individuals include food insecurity, limited healthcare access, mental health challenges, low SES, suboptimal housing, and limited cultural continuity. These findings are relevant to other Arctic regions. It is crucial to understand how SDHs impact the health of Arctic communities and to utilize this information to inform policy and practice decisions for pandemic prevention, management, and treatment. Many SDHs pose challenges for preventing and managing infectious diseases.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.044 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".