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Record W2148642780 · doi:10.1080/01421590600607831

Tuning medical education for rural-ready practice: designing and resourcing optimally

2006· article· en· W2148642780 on OpenAlexfundno aff
Moira Maley, Harriet Denz‐Penhey, Vanessa Lockyer-Stevens, Jamie Murdoch

Bibliographic record

VenueMedical Teacher · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
FundersFaculty of Medicine and Dentistry, University of AlbertaAustralian Government
KeywordsFormative assessmentDisconnectionMedical educationCurriculumGovernment (linguistics)BenchmarkingPsychologyIndoctrinationRural areaPedagogyMedicineMathematics educationPolitical scienceBusiness

Abstract

fetched live from OpenAlex

In an effort to bring doctors back to the bush the Australian government has resourced a number of rural clinical schools (RCS). At the RCS in the University of Western Australia students were allocated in small groups to rural sites for the entire fifth year of a six-year course, sitting the same final examinations as city students. Key factors guiding the successful outcome were the resourcing and implementation of the infrastructure and teaching and learning pedagogy. In designing support, the disconnection of students from their city colleagues was anticipated as an issue, as was the pedagogical indoctrination of the teachers. The curriculum implementation was adapted in this light. The role of the Web in teaching and learning, and their status as 'student colleagues' and independent learners were pivotal aspects. As students settled at their site, their confidence grew and their anxiety over urban disconnection dissipated. By benchmarking themselves using Web-based formative assessments and in formative 'objective structured clinical examinations' staged for them by the RCS, the students received ongoing feedback on their progress. This model of embedding students in rural centres for an extended period with rural practitioners as teachers was successfully implemented at multiple sites geographically vastly separate.

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)0.005
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient 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 score0.392
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.463
Teacher spread0.423 · 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

Citations24
Published2006
Admission routes1
Has abstractyes

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