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Record W2183201155

Big cities and bright lights: rural- and northern-trained physicians in urban practice.

2007· article· en· W2183201155 on OpenAlexaffabout
Raymond Pong, Benjamin T.B. Chan, Tom Crichton, James Goertzen, William McCready

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsNOSM University
Fundersnot available
KeywordsPopularitySpouseMedical educationWork (physics)Rural areaQualitative researchMedical practiceFamily medicinePsychologyMedicineSociologySocial psychologySocial science
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural medical education is increasing in popularity in Canada. This study examines why some family physicians who completed their residency training in northern Ontario decided to practise in urban centres. METHODS: We used a qualitative research method. We interviewed 14 graduates of the Family Medicine North program and the Northeastern Ontario Family Medicine program. The interview transcripts were content-analyzed. RESULTS: There were different pathways leading to urban practice. While some pathways were straightforward, others were more complicated. Most participants offered multiple reasons for choosing to work in urban areas, suggesting that the decision-making processes could be quite complex. Family and personal factors were most frequently mentioned as reasons for choosing the urban option. The needs of the spouse and the children were especially important. Most of the participants had no plans to return to rural medical practice, but even these physicians retained some vestiges of rural practice. CONCLUSION: Most Canadian medical schools now offer some rural medical training opportunities. The findings of this study provide some useful insights that could help medical educators and decision-makers know what to expect and understand how practice location decisions are made by doctors.

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 score0.370
Threshold uncertainty score0.706

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 studies0.0000.000
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 head0.029
GPT teacher head0.345
Teacher spread0.316 · 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

Citations12
Published2007
Admission routes2
Has abstractyes

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