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Record W4399928947 · doi:10.22605/rrh8725

The role, the risk, and the reciprocity: creating positive early rural placements in medical education

2024· article· en· W4399928947 on OpenAlexaffabout
Button, Bohonis, Ross, Kilbertus, Taylor, Cameron

Bibliographic record

VenueRural and Remote Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsNOSM UniversityUniversity of WinnipegLakehead University
Fundersnot available
KeywordsReciprocity (cultural anthropology)MedicinePsychologyMedical educationPedagogySocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The Northern Ontario School of Medicine University seeks to address rural physician shortages in Northern Ontario. One key strategy the school employs is the use of experiential learning placements embedded throughout its undergraduate curriculum. In second year, students embark on two 4-week placements in rural and remote communities. This study sought to explore the factors that contribute to a positive learning experience from the preceptor's perspective. METHODS: Semi-structured interviews were conducted with five community preceptors who have participated in these placements. Using the information from these interviews a survey was created and sent to another 15 preceptors. Data were analyzed using qualitative methods and frequencies. RESULTS: Three key themes were identified from both the interviews and survey data: the role of early rural and remote placements; the risks of these placements; and the need for a reciprocal relationship between institutions, preceptors, and students to create a positive learning environment. CONCLUSION: Preceptors value the opportunity to teach students, but the aims of these placements are not clear and preceptors and local hospitals need more workforce resources to make these experiences positive.

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)0.004
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: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
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 head0.011
GPT teacher head0.394
Teacher spread0.383 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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