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Record W2913703206 · doi:10.1177/2382120518818844

Establishment of an Accelerated Doctor of Family Medicine Program at Unaizah College of Medicine, Qassim University, Kingdom of Saudi Arabia

2019· article· en· W2913703206 on OpenAlexaboutno aff
Ahmad I. Al‐Shafei, Saleh Al‐Damegh, Fahad Almatham, Abdulrahman Al-Mohaimeed, Abdullah Al‐Nafeesah, Ahmad Hamad-Aldosary, Moteb Alotaibi, Osama Al Wutayd, Ali Mansour, Ola A. El‐Gendy, Walaa A. Fadda, Fayig El-Migdadi, Khalid I. AlQumaizi, Sami Shaban

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsKingdomFamily medicineMedicineBiology

Abstract

fetched live from OpenAlex

Primary health care is well known to be the cornerstone for the health of the society. Furthermore, efficient health care at the secondary and tertiary levels is entirely dependent on effective primary health care. The Kingdom of Saudi Arabia (KSA) is currently building up a rigorous primary health care system with a large number of well-equipped primary health care centers. However, there is an acute shortage of Saudi family physicians throughout the country; both in urban and rural areas. There is no evidence in the literature supporting the relatively long 7 years' traditional duration of medical programs in the KSA. Rather, several US and Canadian medical schools have established accelerated programs in Internal Medicine and Family Medicine with graduates comparable with those of the traditional curricula in terms of standardized tests, initial resident characteristics, and performance outcomes. In response to the challenges the KSA is facing in primary health care, Unaizah College of Medicine at Qassim University is proposing to establish an accelerated Doctor of Family Medicine Program that would run for total duration of 6 years. Herein, we describe a concise outline of this program.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.351
Teacher spread0.322 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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