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Record W2102042263

Use of walk-in clinics by rural and urban patients.

2000· article· en· W2102042263 on OpenAlexaffabout
Olga Szafran, Neil R. Bell

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineFamily medicineWalk-inUrban communityRural areaPrimary careRural communityDemographyAlternative medicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare use of walk-in clinics by rural and urban family practice patients and to describe patients' perceptions of the quality of care in physicians' offices. DESIGN: Questionnaire completed by patients in family physicians' offices. SETTING: Nine community-based family practices located in rural and urban areas of Alberta. PARTICIPANTS: Patients who had visited their family physicians' offices during April, May, June, or July 1997. Response rate was 89.6% (403 of 450 questionnaires were completed). MAIN OUTCOME MEASURES: Use of walk-in clinics, patients' perceptions of the quality of care in physicians' offices. RESULTS: Overall, 27.5% of patients (22.2% of rural, 35.5% of urban patients) attended walk-in clinics in the 6 months before visiting their family physicians' offices: 43.3% went during weekdays when their family physicians' offices were open. Significantly more rural (91.1%) than urban (60.7%) patients felt they could contact their doctors during evenings and weekends (P.004). Significantly more urban (67.2%) than rural (33.3%) patients did not call their own physicians before going to walk-in clinics (P.002). Patients who attended walk-in clinics were more likely (P.01) than patients who did not to rate their family physicians' office hours poor to good (27.9% vs 15.6%). CONCLUSIONS: Many patients attending the offices of community-based family physicians in both urban and rural areas of Alberta also attend walk-in clinics. Family practice patients attend walk-in clinics primarily because their own physicians' offices are less convenient.

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)0.000
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 score0.221
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.057
GPT teacher head0.357
Teacher spread0.300 · 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

Citations49
Published2000
Admission routes2
Has abstractyes

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