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Record W3006346408 · doi:10.59284/jgpeman149

Historical evolution and present status of general practice in Nepal

2014· article· en· W3006346408 on OpenAlexaboutno aff
Bruce Hayes, Ashis Shrestha

Bibliographic record

VenueJournal of General Practice and Emergency Medicine of Nepal · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryGeographyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUNDIn 1981, in the face of no post-graduate training programs, health policy makers realized that Nepal needed "generalist" physicians who could cope with the wide range of preventive and curative medicine required in rural areas.From discussions between His Majesty's Government (HMG) and the University of Calgary, the Medical Doctorate in General Practice (MDGP) programme was launched in 1982 by the Institute of Medicine (IOM), Tribhuvan University and the Ministry of Health (MOH).Phase 1 was from 1982-1987 when half the training (18 months) was in Calgary, Canada with the rest in Nepal, including 6 months in Surkhet in mid-western Nepal.A total of 11 doctors undertook at least part of the training programme, with 7 completing the training and 6 passing the final examination, receiving a Postgraduate Diploma in General Practice.Phase 2 was from 1987-1988 with 3 months training in Malaysia.A further six doctors successfully completed training to receive the MD (GP) degree.During 1989 and 1990, the program accepted no new intake of trainees while the University re-evaluated the feasibility and requirements of the programme.Phase 3 started in 1991 continues to the current time with all training in Nepal.In a 1994 evaluation, it was recommended to establish linkages between the IOM and Patan Hospital to supplement weaknesses in the curriculum, provide resources for facility development and provide the basis for future collaboration.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.323
Teacher spread0.278 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

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