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Record W169425801

The experience of primary health care users: a rural-urban paradox.

2010· article· en· W169425801 on OpenAlexaffabout
Paul A. Lamarche, Raynald Pineault, Jeannie Haggerty, Marjolaine Hamel, Jean‐Frédéric Lévesque, Josée Gauthier

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsContext (archaeology)Primary careRural areaHealth carePrimary health carePatient experienceMedicineNursingBusinessPsychologyGeographyFamily medicineEnvironmental healthEconomic growthPopulation
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: We sought to assess the care experience of primary health care users, to determine whether users' assessments of their experience vary according to the geographical context in which services are obtained, and to determine whether the observed variations are consistent across all components of the care experience. METHODS: We examined the experience of 3389 users of primary care in 5 administrative regions in Quebec, focusing on accessibility, continuity, responsiveness and reported use of health services. RESULTS: We found significant variations in users' assessments of the specific components of the care experience. Access to primary health care received positive evaluations least frequently, and continuity of information received the approval of the highest percentage of users. We also found significant variations among geographical contexts. Positive assessments of the care experience were more frequently made by users in remote rural settings; they became progressively less frequent in near-urban rural and near-urban settings, and were found least often in urban settings. We observed these differences in almost all of the components of the care experience. CONCLUSION: Given the relatively greater supply of services in urban areas, this analysis has revealed a rural-urban paradox in the care experience of primary health care users.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.369
Teacher spread0.326 · 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 designNot applicable
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

Citations27
Published2010
Admission routes2
Has abstractyes

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