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Record W174616693

Determinants of health-service use by low-income people.

2005· article· en· W174616693 on OpenAlexaffabout
Moira Stewart, Linda Reutter, Edward Makwarimba, Irving Rootman, Deanna L. Williamson, Kim D. Raine, Doug Wilson, Janet Fast, Rhonda Love, Sharon McFall, Deana Shorten, Nicole Letourneau, Karen Hayward, Jeff Masuda, William Rutakumwa

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPovertyThematic analysisLife expectancyHealth servicesBusinessEnvironmental healthEconomic growthQualitative researchMedicineSociologyPopulationEconomics
DOInot available

Abstract

fetched live from OpenAlex

Poverty influences health status, life expectancy, health behaviours, and use of health services. This study examined factors influencing the use of health-related services by people living in poverty. In the first phase, 199 impoverished users of health-related services in 2 large Canadian cities were interviewed by their peers. In the second phase, group interviews with people living in poverty (n = 52) were conducted. Data were analyzed using thematic content analysis. Diverse health-related services were used to meet basic and health needs, to maintain human contact, and to cope with life's challenges. Use of services depended on proximity, affordability, convenience, information, and providers' attitudes and behaviours. Use was impeded by inequities based on income status. To promote the health of people living in poverty, nurses and other health professionals can enhance the accessibility and quality of services, improve their interactions with people living in poverty, provide information about available programs, offer coordinated community-based services, collaborate with other sectors, and advocate for more equitable services and policies.

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)0.000
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 score0.143
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.378
Teacher spread0.317 · 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

Citations41
Published2005
Admission routes2
Has abstractyes

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