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Record W2954749139 · doi:10.1177/2382120519859311

A Qualitative Investigation of the Experiences of Students and Preceptors Taking Part in Remote and Rural Community Experiential Placements During Early Medical Training

2019· article· en· W2954749139 on OpenAlexaff
Brian Ross, Erin Cameron, David Greenwood

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsNOSM UniversityLakehead University
Fundersnot available
KeywordsExperiential learningMedical educationPsychologyTraining (meteorology)Qualitative researchMedicinePedagogySociologyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Medical education can help alleviate the chronic undersupply of physicians to rural communities. Providing students with early rural clinical experiences may allow the gaining of necessary knowledge and skills to practice and live rurally, as well as the desire to do so. PURPOSE: This study aims to provide a detailed understanding of Remote and Rural Community Placements (RRCPs) which occur in the second year of a Doctor of Medicine programme. METHODOLOGY/APPROACH: Using a thematic analysis approach, we examined the experiences of students and preceptors in the RRCP. Data were collected using semi-structured interviews and focus groups. FINDINGS/CONCLUSIONS: Students valued RRCPs as a formative clinical experience and preceptors gained professionally from participating. The RRCPs enhanced students regard for, and knowledge of, rural medicine. Yet, contrary to the stated aims of the placement, students spent very little time in activities outside of the clinic, neither learning about the community nor about the life of a physician as a community member. IMPLICATIONS: Medical educators should recognise that students and preceptors will inevitably place different value on the different sociocultural and perceptual aspects of placements, namely clinical and non-clinical. As such, the curriculum should draw clearly articulated links between each.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.265

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 studies0.0000.000
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 head0.070
GPT teacher head0.488
Teacher spread0.417 · 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 designQualitative
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

Citations10
Published2019
Admission routes1
Has abstractyes

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