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Record W1830409718

Future practice location and satisfaction with rural medical education: survey of medical students.

2006· article· en· W1830409718 on OpenAlexaffabout
Farrah J. Mateen

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRural areaMedical educationPrimary careMedicineFamily medicineRural healthRural populationPopulationWork (physics)NursingEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the desired future practice location of a cohort of medical students with strong rural representation and to inquire whether they were satisfied with their medical experiences in rural primary care settings. DESIGN: Survey questionnaire. SETTING: The College of Medicine at the University of Saskatchewan in Saskatoon. PARTICIPANTS: One hundred twenty-two medical students. MAIN OUTCOME MEASURES: Demographic information, plans for future practice, and opinions on rural medical experiences in primary care settings. RESULTS: Although students from both rural and non-rural backgrounds were highly satisfied with mandatory and voluntary rural experiences and considered them valuable for their medical education, fewer than 10% of the 122 students desired to work in centres with less than 10 000 population. Only 2 students hoped to practise in such locations. Most students interested in family practice were interested in urban practice, and most students from rural areas were not interested in rural practice. CONCLUSION: Both rural and non-rural students were highly satisfied with their medical education in rural primary care settings, but this did not mean either group wanted to practise in rural settings. Demographic profiling of students (to ascertain whether they have rural origins) and assessing satisfaction with rural medical education give only partial information on who might choose to practise family medicine in rural areas.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.081
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 head0.024
GPT teacher head0.417
Teacher spread0.392 · 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

Citations13
Published2006
Admission routes2
Has abstractyes

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