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Record W4386572559 · doi:10.32799/ijih.v18i2.39431

COVID-19 Containment in Indigenous Communities in North-West Saskatchewan: Community and Multi-Sectoral Stakeholder Perspectives

2023· article· en· W4386572559 on OpenAlexafffundvenueabout
Tracey Carr, Stephanie Witham, Anum Ali, Erin Lashta, Teddy Clark, Leonard Montgrand, Martha Morin, Robert St. Pierre, Marissa Evans, Gary Groot

Bibliographic record

VenueInternational Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMétis National CouncilUniversity of SaskatchewanSaskatchewan Health
FundersSaskatchewan Health Research Foundation
KeywordsIndigenousPreparednessPublic healthThematic analysisStakeholderPublic relationsGovernment (linguistics)Political scienceCommunity resilienceCommunity engagementStakeholder engagementLaw enforcementPublic administrationEconomic growthQualitative researchSociologyMedicineNursingResource (disambiguation)

Abstract

fetched live from OpenAlex

In the spring of 2020, remote Indigenous communities in the far north-western region of Saskatchewan, Canada, experienced a COVID-19 outbreak that required the collaboration of local leaders, Indigenous governments, and other multi-sectoral organizations. This study shares the stories of those involved in the response and illustrates the challenges, successes, and recommendations for future emergency preparedness. A total of 22 participants were interviewed from the impacted communities, government agencies, and organizations in public health, public safety, and law enforcement between May and August of 2021. Qualitative interviews were analyzed using thematic analysis resulting in the following themes: 1) Challenges, 2) Consequences, 3) Successes, and 4) Recommendations. A final knowledge translation event was held with key stakeholders, including public health professionals and community members, to co-create final recommendations for future public health responses in remote Indigenous communities. Our findings underscored the importance of community leadership, local investment, public health preparedness, and relationship building between organizations and jurisdictions. Lessons and recommendations from these stories can be applied to future pandemic preparedness in the province.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.197
GPT teacher head0.460
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes4
Has abstractyes

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