MétaCan
Menu
Back to cohort
Record W3004683261 · doi:10.12927/hcpol.2019.26069

What Changes Would Manitoba First Nations Like to See in the Primary Healthcare They Receive? A Qualitative Investigation

2019· article· en· W3004683261 on OpenAlexaffvenueabout
Grace Kyoon‐Achan, Josée G. Lavoie, Wanda Phillips-Beck, Kathi Avery Kinew, Naser Ibrahim, Stephanie Sinclair, Alan Katz

Bibliographic record

VenueHealthcare policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaManitoba HealthFirst Nations Health and Social Secretariat of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsVisionHealth carePrimary health careQualitative researchPublic relationsPolitical scienceNursingSociologyMedicineSocial scienceLaw

Abstract

fetched live from OpenAlex

BACKGROUND: First Nations (FN) have unique perspectives and experiences of health and healthcare services, which are critical to the provision of effective community-based primary healthcare (CBPHC). OBJECTIVE: This paper shares FN perspectives on primary healthcare (PHC), taking geographical, cultural and historical realities into account, to elucidate opportunities to improve current healthcare services. METHODS: Semi-structured in-depth qualitative interviews were completed with 183 residents of 8 Manitoba FN communities. Grounded theory-guided data analysis was conducted. RESULTS: Improving PHC performance requires delivering timely and holistic healthcare that integrates traditional health knowledge, comprehensive CBPHC increasing services such as healthcare and medical transportation, healthy food as an important preventative measure and a culturally informed workforce backed by local leadership and promoting cultural respect. CONCLUSION: The relationship between self-determination and health is a critical factor in the implementation of CBPHC. FN must be respected to decide healthcare priorities that reflect the needs and visions of each community.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.473
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.396
Teacher spread0.328 · 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 designQualitative
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

Citations13
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueHealthcare policySame topicIndigenous Health, Education, and RightsFrench-language works237,207