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Care interrupted: Poverty, in-migration, and primary care in rural resource towns

2017· article· en· W2752733351 on OpenAlexafffundabout
Kathleen Rice, Fiona Webster

Bibliographic record

VenueSocial Science & Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsInstitute for Work & HealthUniversity of TorontoPublic Health Ontario
FundersCanadian Institutes of Health Research
KeywordsPovertyNursingHealth careRural areaFieldnotesQualitative researchMedicineEconomic growthBusinessSociologyEthnography

Abstract

fetched live from OpenAlex

Internationally, rural people have poorer health outcomes relative to their urban counterparts, and primary care providers face particular challenges in rural and remote regions. Drawing on ethnographic fieldnotes and 14 open-ended qualitative interviews with care providers and chronic pain patients in two remote resource communities in Northern Ontario, Canada, this article examines the challenges involved in providing and receiving primary care for complex chronic conditions in these communities. Both towns struggle with high unemployment in the aftermath of industry closure, and are characterized by an abundance of affordable housing. Many of the challenges that care providers face and that patients experience are well-documented in Canadian and international literature on rural and remote health, and health care in resource towns (e.g. lack of specialized care, difficulty with recruitment and retention of care providers, heavy workload for existing care providers). However, our study also documents the recent in-migration of low-income, largely working-age people with complex chronic conditions who are drawn to the region by the low cost of housing. We discuss the ways in which the needs of these in-migrants compound existing challenges to rural primary care provision. To our knowledge, our study is the first to document both this migration trend, and the implications of this for primary care. In the interest of patient health and care provider well-being, existing health and social services will likely need to be expanded to meet the needs of these in-migrants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.440
Teacher spread0.405 · 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

Citations14
Published2017
Admission routes3
Has abstractyes

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