MétaCan
Menu
Back to cohort
Record W2187085784

Survey of rural family physician-obstetricians in Southwestern Ontario.

2007· article· en· W2187085784 on OpenAlexaboutno aff
Neal Stretch, Andrea Voisin, Shannon Dunlop

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNursingWork (physics)Family medicineMedical educationProfessional development
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The objectives of this paper are 1) to analyze the characteristics of rural physicians who currently practise obstetrics, their training background and the environment in which they work and 2) to develop strategies to sustain rural obstetrical services. METHODS: Information was gathered using both a survey and brief individual interviews. PARTICIPANTS: A survey was sent to 56 family physicians who currently practise obstetrics,as well as those who had stopped within the past 2 years, in the Southwestern Ontario communities of Clinton, Goderich, Hanover, Kincardine, Markdale, Mount Forest, Palmerston, Walkerton, Wiarton and Wingham. RESULTS: Forty-four physicians responded to the survey. Results indicate that current obstetrical training programs are lacking in the following areas: the provision of positive role models/mentors, rural placements, experience in complex decision-making, and instilling confidence in graduates. Physicians appear to be internally motivated to practise obstetrics, claiming it is important to their professional goals and personal values. Support systems of colleagues, nursing staff, administration, family and friends, were identified as vital components of a successful obstetrical program. CONCLUSION: Educators are advised to identify students with an internal motivation to practise rural obstetrics early in their medical training and provide them with mentors, rural placements, confidence and experience in complex decision-making.

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)0.003
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 score0.337
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.080
GPT teacher head0.369
Teacher spread0.289 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicGlobal Health Workforce IssuesFrench-language works237,207