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Record W4393346315 · doi:10.1016/j.ssmqr.2024.100424

Factors influencing medical adherence among First Nations patients and patients of European ancestry: Data from Canada

2024· article· en· W4393346315 on OpenAlexafffundabout
Annabel Levesque, Mitch Verde, Han Z. Li, Bin Yu, Xinguang Chen

Bibliographic record

VenueSSM - Qualitative Research in Health · 2024
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Northern British ColumbiaUniversité de Saint-Boniface
FundersUniversity of Northern British Columbia
KeywordsSituational ethicsContext (archaeology)Isolation (microbiology)Face (sociological concept)Health careHealthcare systemMedicineFamily medicineNursingPsychologyGeographyPolitical scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

Nonadherence to physicians’ recommendations can have a detrimental impact on patient health, to say nothing of the financial cost to the already unsustainable Canadian healthcare system. This comparative study aimed at gaining a deeper understanding of the factors influencing adherence to prescribed medications and lifestyle change recommendations among First Nations patients and patients of European ancestry. In-depth, face-to-face interviews were conducted with 40 participants in Northern British Columbia, Canada. Interviews were transcribed and qualitatively analyzed. Results show that medical adherence derives from an interaction between personal factors and situational or external factors. A comparative analysis revealed that a disproportionate number of First Nations patients faced situational barriers that impeded with medical adherence. These factors include geographical isolation, lack of access to a regular doctor, negative healthcare experiences, and financial constraints. Analyzed through a postcolonial interpretive lens, the research findings highlight the need to reduce systemic barriers within the healthcare system and the wider social context, especially among First Nations patients living in remote communities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.333
GPT teacher head0.537
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes3
Has abstractyes

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