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Record W2979859280

Physician recruitment and retention in Manitoba: results from a survey of physicians' preferences for rural jobs.

2017· article· en· W2979859280 on OpenAlexaffabout
Julia Witt

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsWorkforceIncentiveJob satisfactionDemographic economicsCompensation (psychology)PsychologyGerontologySocioeconomicsBusinessMedicineSociologyEconomicsEconomic growthSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural recruitment and retention continues to present challenges to health workforce planners. This paper reports and analyzes the results of a survey sent to physicians in Manitoba, eliciting their opinions regarding rural jobs. METHODS: A survey was sent to all physicians in Manitoba. Part 1 of the survey included questions about background and demographic information; part 2 was a set of job satisfaction questions regarding respondents' current job; and part 3 included 2 sets of stated-choice questions eliciting preferences for a set of attributes relevant to rural recruitment and retention. RESULTS: Of the 2487 physicians who received surveys, 561 (22.6%) responded. Respondents indicated that income, hours worked and on-call frequency are very important: overall job satisfaction increased with income and decreased with hours worked. Income, hours and on-call frequency were ranked "very important" by the largest proportions of physicians. The estimated compensation for on-call more frequent than 1-in-4 was very high (82% of average income), and additional hours worked were worth $183 per hour. Other attributes that were important included professional interaction, housing availability and community incentives during the first year, which were valued at 11%-31% of annual income. CONCLUSION: Work-life balance is a key consideration for rural jobs, and there are incentives that can compensate for less desirable attributes.

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)0.001
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 score0.348
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.307
GPT teacher head0.427
Teacher spread0.120 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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