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Record W2127520598

Access to family physicians in southwestern ontario.

2009· article· en· W2127520598 on OpenAlexaffabout
Graham J. Reid, Thomas R. Freeman, Amardeep Thind, Moira Stewart, Judith Belle Brown, Evelyn Vingilis

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsPopulationEconomic shortageMedicineDemographyStratified samplingFamily medicineHealth careEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Shortages of family physicians (FPs) have been reported, but accurate data on the scope of this problem are sparse. The study objective was to determine the proportion of the population in southwestern Ontario without access to a regular FP and sources of usual medical care for individuals with and without a regular FP. METHOD: Random-digit dialling was used to obtain a stratified, random sample of households from 10 counties in southwestern Ontario, which resulted in 1,387 participants (60.5% cooperation rate). Adults reported on themselves, while a random selection of parents reported on their children, yielding data on individuals ranging from 0 to 95 years of age. RESULTS: 9.1% (95% CI = 7.8% to 10.6%) of individuals did not have a regular FP. Most individuals without a regular FP used walk-in clinics (55%) or emergency rooms (13%) as their usual source of care, while 5.9% reported not receiving medical care. Lack of physicians accepting new patients was the most common reason for not having a regular FP (27%), although some individuals chose not to have one (9.9%) or had alternative access to care (13.2%). CONCLUSIONS: Based on the assumption that the individuals who chose not to have a FP, or who had access to alternative care, would continue not to want a FP if one were available, we estimate that 5.1% of the population of southwestern Ontario requires a FP. The health implications of not having a regular FP in Canada need to be examined.

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 score0.234
Threshold uncertainty score0.961

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 integrity0.0000.001
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 head0.092
GPT teacher head0.406
Teacher spread0.314 · 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

Citations21
Published2009
Admission routes2
Has abstractyes

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