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Record W2146210412

Short report: Scope of family practice in rural and urban settings.

2004· article· en· W2146210412 on OpenAlexaffabout
Peter Hutten‐Czapski, Roger Pitblado, Steve Slade

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsReferralLogistic regressionCensusPopulationGeographyDemographyScope of practiceVariety (cybernetics)Family medicineMedicineStatisticsMathematicsSociologyHealth carePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

amily physicians in all settings provide a variety of procedures.1,2 Rural doctors tend to provide a greater number or variety of procedures than their urban counterparts.1,2 What has not been studied well is where practice patterns diff er on the rural-urban continuum and the relative importance of variables that contribute to the diff erences. Our hypothesis was that geography is the predominant predictor of scope of practice and might be a more signifi cant predictor than physicians’ age1,2 or sex.3 Primary data source for this study was the 1997 College of Family Physicians of Canada’s National Family Physician Survey.4 A practice breadth score was calculated by totaling the survey responses from each of 16 questions on procedures and eight on on-call activities. Geographic location of practices was determined using the postal codes provided by the 2981 respondents matched with 1996 census data and geography. Straight-line distances were computed between practice location and nearest hospitals and communities of various sizes. Th e practice breadth score was modeled using independent variables: sex, age, practice type, straight-line distance to large referral hospital (>299 beds), municipal population size, and general region (Atlantic, Quebec, Ontario, Prairies, Alberta, British Columbia, Northern). Multivariate logistic regression was done to confi rm independence of variables and to determine the relative weight of each parameter in determining the total practice breadth score. A sum of squares analysis was done to determine how close the model fi t to observed variation in practice breadth. In smaller centres, 21 of the 24 procedures and on-call items were found to be more common (Table 1). When all 24 items were combined in the practice breadth score, a progression (Figure 1) was noted in association with increasing distance from a large city (>100 000 population). A multivariate statistical model based on factors of sex, age, practice type, distance to large hospital, municipal population, and region was found to explain 38% of the variation in practice breadth score. Pearson correlates to practice breadth were strongest for the geographic variables of distance to large hospital (0.401, P < .01), community size (-0.363, P < .01) and region (0.184, P < .01), which together accounted for 30% of the variation. An additional 8% of the variation in practice breadth was explained by personal characteristics of sex (0.172, P < .01), age (-0.123, P < .01), and type of medical practice (-0.083, P < .01). Our analyses suggest that, as geographic isolation increases, Canadian family physicians provide an increasingly broad spectrum of services. Our study confi rms earlier work that male sex,3 youth of physician,1,2 and FP group practice5 are associated with increased breadth of practice. Focusing Dr Hutten-Czapski is a family physician in rural practice in Haileybury, Ont. He is an Assistant Professor at the University of Ottawa and at the Northern Ontario School of Medicine. Dr Pitblado is a Professor of Geography at Laurentian University in Sudbury, Ont. Mr Slade is a research consultant at the Canadian Institute for Health Information.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.003

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 head0.046
GPT teacher head0.391
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), 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

Citations52
Published2004
Admission routes2
Has abstractyes

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