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Record W4200284240 · doi:10.4103/cjrm.cjrm_2_21

Why they leave: Small town rural realities of northern physician turnover

2021· article· en· W4200284240 on OpenAlexaffvenueabout
Eliseo Orrantia, Jilayne Jolicoeur, Lily DeMiglio

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsNOSM University
Fundersnot available
KeywordsThematic analysisBurnoutNursingTurnoverFamily medicineMedicineMedical educationPsychologyQualitative researchPolitical scienceSociologyManagement

Abstract

fetched live from OpenAlex

INTRODUCTION: This study seeks to explore influential factors leading to physician turnover in designated Rural Northern Physician Group Agreement (RNPGA) communities in Northern Ontario, as well as physician's perceptions of the RNPGA contract and effects of the Northern Ontario School of Medicine (NOSM) on physician retention in these communities. METHODS: Twelve qualitative semi-structured interviews were completed with rural physicians who had RNPGA contracts within the past 5 years but had left their practice community. Data collected from recorded interviews were analysed using a thematic analysis approach in order to identify common themes. RESULTS: A range of factors influencing physician's decisions to leave were identified including lack of partner career prospects, burnout and lack of opportunities and amenities. Common challenges were sometimes also perceived as rewards of rural practice. The concern of lack of flexibility of the RNPGA contract was identified, as well as a perceived lack of presence of NOSM graduates in RNPGA communities. CONCLUSION: A variety of factors influence physician turnover in RNPGA communities. These may be considered by communities hoping to inform recruitment and retention policy. Renewal of the RNPGA contract may require consideration for availability of part-time positions, increasing the number of physicians funded and incentivising physician wellness. NOSM may consider mandatory postgraduate programme placements in RNPGA communities and further development of infrastructure in these communities to improve learner, graduate and institutional engagement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.040
GPT teacher head0.354
Teacher spread0.314 · 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.

Study designNot applicable
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

Citations22
Published2021
Admission routes3
Has abstractyes

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