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Record W2081630811 · doi:10.2147/amep.s69217

Evaluation of the orthopedic residency training program in Saudi Arabia and comparison with a selected Canadian residency program

2014· article· en· W2081630811 on OpenAlexaboutno aff
Hamza M. Alrabai, Abdulaziz Al-Ahaideb, Osama Alrehaili, Abdulaziz Aljurayyan, Ranyah Alsaif, Nizar Algarni, Hazem Al-Khawashki, Abdulrahman Algarni

Bibliographic record

VenueAdvances in Medical Education and Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipMedicineResidency trainingFamily medicineMedical educationContinuing education

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary aim of the present study was to assess the quality of the Saudi Orthopedic Residency Program. METHODOLOGY: As a comparator, a cross-sectional survey involving 76 Saudi residents from different training centers in Saudi Arabia namely; Riyadh, Jeddah, Medina, Abha, and Dammam and 15 Canadian. RESULTS: The results showed that Canadian residents read more peer-reviewed, scholarly articles compared with Saudi residents (P=0.002). The primary surgical role for residents was to hold retractors during surgery. The survey respondents strongly supported the ability to recommend removal of incompetent trainers. Saudi trainees were more apprehensive of examinations than Canadian trainees (P<0.0001). Most residents preferred studying multiple-choice questions before examinations. Saudi and Canadian participants considered their programs to be overcrowded. Unlike Canadian participants, Saudi trainees reported an inadequate level of training (P<0.0001). CONCLUSION: Educational resources should be readily accessible and a mentorship system monitoring residents' progress should be developed. The role of the resident must be clearly defined and resident feedback should not be ignored. Given the importance of mastering basic orthopedic operative skills for residents, meaningful remedial action should be taken with incompetent trainers.

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)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.441
Teacher spread0.394 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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