The health impacts of social distancing among Indigenous People in Ontario during the first wave of COVID-19
Bibliographic record
Abstract
Among Indigenous People in Canada and around the world, the health impacts of COVID-19 have been measured largely through biological, social, and psychological impacts. Our study draws from a relational concept of health to examines two objectives: 1) how social distancing protocols have shaped Indigenous connections with self, family, wider community, and nature; and 2) to exploring what these changing relationships mean for perceptions of health. Carried out by an Indigenous team of scholars, community activists and students, this research draws from a decolonizing methodology and qualitative interviews (n=16) with Indigenous health and social care providers in urban and reserve settings. Our results illustrate a considerable decline in interpersonal connections, such as with family, community organizations, and larger social networks, as a result of social distancing. Among those already vulnerable, underlying health, social, and economic inequities have been exacerbated. While the health impacts of COVID-19 have been overwhelmingly negative, participants noted the importance of this time for self-reflection and reconnection of human-kind and with mother earth. This paper offers an alternative perspective to popularized views of Indigenous experiences of COVID-19 as they relate to vulnerability and resilience.
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 | 0.015 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".