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Record W4411779196 · doi:10.22605/rrh9355

A rural practice affinity model: recognizing the role of emergency medicine competency

2025· article· en· W4411779196 on OpenAlexafffundabout
Eliseo Orrantia, Theresa J. B. Kline, Lindsay Nutbrown, Erin K. Cameron, Margaret Cousins

Bibliographic record

VenueRural and Remote Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of CalgaryNOSM University
FundersNorthern Ontario Academic Medicine Association
KeywordsMedical educationMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural Canadians have poorer health indices than their urban counterparts and struggle with worse access to care due to an undersupply of physicians. Research has identified personal factors, such as being raised in a rural environment, and traits, such as lower harm avoidance, among those drawn to rural practice. As well, the impact of aspects of medical training, such as rural rotations, have been recognized in creating rural practice intentions, but the role of specific clinical competencies here has yet to be determined. Emergency medicine is often one of the most challenging components of rural practice and thought by some to have its competencies poorly developed in family practice training. We hypothesized a model for rural practice affinity in which a strong sense of general self-efficacy would be independently mediated by the development of emergency medicine competence and rural practice self-efficacy, leading to stronger intentions to embark on a rural practice career. METHODS: This model was tested using the data from a survey of all family medicine residents nearing graduation from 14 of the 17 Canadian medical schools. Demographics and data on factors known to influence a rural career choice were collected and accounted for when determining the strength of the hypothesized relationships. Both existing and specifically designed survey tools were used to assess model components. A partial correlation matrix between the variables of interest (general self-efficacy, emergency medicine competency, rural practice self-efficacy, and rural practice intentions) - controlling for the effects of relationships, financial aspects, personal aspects, and social desirability - was created and subjected to a structural equation model. RESULTS: Our initial rural practice affinity model resulted in a poor fit of the model to the data. However, the addition of a pathway from emergency medicine competence to rural practice self-efficacy improved the model to one showing significant paths as hypothesized as well as excellent measures of fit. DISCUSSION: The importance of general self-efficacy is recognized and is itself mediated by the more specific rural practice self-efficacy to rural practice intentions, consistent with the literature. Emergency medicine competency has a central role in both mediating general self-efficacy to rural practice intentions, while also being mediated itself by rural practice self-efficacy to rural practice intentions. This provides new understanding in the development of rural practice self-efficacy. The link of emergency medicine competency to both rural practice self-efficacy and rural practice intentions suggests that this is a curricular area that deserves greater focus and consideration of how to ensure that residents are meeting emergency medicine requirements and receiving robust training in this area. This is especially important as there have been significant concerns from various groups on the efficacy of emergency medicine training in family medicine residency. CONCLUSION: These findings will help inform residency program curriculum and pedagogies, underlining the critical role of emergency medicine competence to support rural physician identity formation and to improve physician recruitment to rural Canada.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.047
GPT teacher head0.445
Teacher spread0.397 · 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 designOther design
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

Citations0
Published2025
Admission routes3
Has abstractyes

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