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Record W962267300 · doi:10.22605/rrh3245

Rural health activism over two decades: the Wonca Working Party on Rural Practice 1992-2012

2015· article· en· W962267300 on OpenAlexaff
Ian Couper, Roger Strasser, James Rourke, John Wynn-Jones

Bibliographic record

VenueRural and Remote Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMemorial University of NewfoundlandNOSM University
Fundersnot available
KeywordsHealth careRural areaEconomic growthRural healthPublic relationsFront linePolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

The Wonca Working Party on Rural Practice (WWPRP) was formed in 1992 in response to the realization that rural healthcare faced many serious and similar challenges around the world. Over the years the members of the committee have come from many different countries but found inspiration and strength in developing and sharing educational and health system innovations that could be modified and applied to different rural settings. The 11 world rural health conferences organized by the WWPRP over the first two decades since it was founded brought together a range of people, from rural doctors and other front-line healthcare workers to administrators and educational leaders, who connected with and learned from each other to advance rural health care around the world. The WWPRP policy documents and conference consensus statements have been important in shaping rural health care in a number of different contexts, and have led to issues of rural health care rising to prominence on the world stage. The WWPRP has throughout been an activist lobby group with a focus on the rural communities it serves rather than its members, and enters its third decade with much left to be done.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0170.006
Scholarly communication0.0100.003
Open science0.0010.013
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0090.001

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 head0.083
GPT teacher head0.470
Teacher spread0.387 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2015
Admission routes1
Has abstractyes

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