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Rural physicians' skills enrichment program: A cohort control study of retention in Alberta

2012· article· en· W1595064987 on OpenAlexaffabout
Ron Gorsche, Wayne Woloschuk

Bibliographic record

VenueAustralian Journal of Rural Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSpecialtyMedicinePsychological interventionFamily medicineRural areaProgram evaluationRural healthNursingGerontology

Abstract

fetched live from OpenAlex

OBJECTIVE: The Rural Physician Action Plan of Alberta introduced an enrichment program in 2001 to improve physician access to skills training. The objective of this study was to evaluate this program and measure retention compared with matched controls over 5 years. DESIGN: Longitudinal, matched, case control study and program evaluation. SETTING: Rural communities in Alberta, Canada. PARTICIPANTS: Rural physicians. INTERVENTIONS: Thirty-one rural physicians self-selected their personal skills training program and listed three goals they wished to attain. They were matched by age, specialty, years in practice and size of community with rural physicians who did not participate in a skills training or upgrading program. MAIN OUTCOME MEASURES: Goal attainment for subject physicians, use of skills at 5 years and comparison of rural retention of physicians at 5 years. RESULTS: Thirty-two of thirty-five physicians classified their goal attainment to be as expected or greater, and all were using their new skills at 5 years. Of the matched physicians, 29 training participants remained in rural practice at 5 years compared with only 22 of 29 matched control: relative risk 1.31, confidence interval 1.06-1.62 P < 0.05. CONCLUSIONS: The enrichment program provides focused, valued skills training for rural physicians and long-term benefits to rural communities.

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)0.002
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: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.430
Teacher spread0.396 · 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

Citations16
Published2012
Admission routes2
Has abstractyes

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