Understanding the System of COVID-19 Vaccination in the Indigenous Communities of the James and Hudson Bay Region of Northern Ontario: A Study of Leadership and Healthcare Provider Perspectives
Bibliographic record
Abstract
The Weeneebayko Area Health Authority (WAHA) services 6 communities in the James and Hudson Bay region of Northern Ontario: Moosonee, Moose Factory, Attawapiskat, Fort Albany, Kashechewan, and Peawanuck. The WAHA collaborated with a multitude of provincial and federal partners to organize phased COVID-19 vaccine rollouts through “Operation Remote Immunity”. There is a lack of information regarding the coordination of clinics in these communities. This thesis investigated the perspectives of leadership and healthcare providers regarding COVID-19 vaccination for communities serviced by the WAHA. The main objectives of this study were to describe the system of vaccination, identify systemic barriers and facilitators to vaccination, and define priorities for improvement within community healthcare systems. Semi-structured interviews were conducted with 17 participants who were involved in the vaccination and pandemic response for these communities. Interview data was analyzed by reflexive thematic analysis. The results showed that leveraging local leadership, collaboration, community-minded approaches, and connection to the land strengthened the pandemic response and vaccination system. However, a lack of health human resources, disjointed communication, urgency and panic, differences among the communities in the region, and context-specific challenges to providing proof of vaccination presented the largest barriers encountered by participants. The findings from this study will be useful in the post-pandemic period and for future public health 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".