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Record W4378781532 · doi:10.1186/s12909-023-04366-2

A comparative study of the curriculum in master degree of medical education in Iran and some selected countries

2023· article· en· W4378781532 on OpenAlexaboutno aff
Somayeh Akbari Farmad, Ali Esfidani, Sara Shahbazi

Bibliographic record

VenueBMC Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsnot available
FundersShahid Beheshti University of Medical Sciences
KeywordsChecklistCurriculumMedical educationData collectionHigher educationPsychologyMedicinePedagogySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: When training efficient human resources for the health system, it is necessary to train appropriate student teaching and assessment methods and necessary skills for educational planning and evaluation. Therefore, studies and efforts to train human resources in the field of Iranian medical education have begun since 1994. The aim of the present study is a comparative study of the curriculum in master's degree of medical education in Iran and some selected countries. METHODS: This is applied, descriptive and comparative research. Data were collected by electronic search on the website of the selected universities. Each of these selected educational curricula, the newest curriculum of the studied universities at the time of the present research, was translated into fluent Persian and studied in detail. The model used in the present study is the famous Polish Bereday model. A quota sampling method was used and universities were selected at a 1:10 ratio from each classified area. Institutions that offered master's degrees in medical education were chosen in each region using the World Health Organization (WHO) classification based on QS World University Rankings (2020). The data collection instrument was a researcher-made checklist, which was used to extract the relevant data available on the website of selected universities. This checklist consisted of eight items, which included course title, course length, mission, vision, goals, admission process, teaching methods (online, In-person, and both), educational strategies, teaching methods, and student assessment. These eight items were compared at selected universities. RESULTS: The samples included seven selected universities including Kebangsaan University in Asia, the University of Toronto in Canada, the University of Michigan in America, the University of Bern and Imperial College in Europe, Monash University in Australia, and Iranian universities. Student admission in Iran is carried out through a centralized exam; therefore, most faculties do not have the option to select students and criteria for student selection. The course length in all universities is between 1-3 years (depending on part-time/full-time) and most of the studied universities offer this field as modular degree courses. CONCLUSIONS: The characteristics of the curriculum for the master's degree of medical education in Iran and selected countries showed the differences and similarities of this course among the top universities of different continents. Unlike other countries, the curriculum for a master's degree in medical education in Iran is offered in a centralized manner in eight universities of Tehran, Shahid Beheshti, Iran, Isfahan, Shiraz, Tabriz, Kerman, and Mashhad.

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)0.001
metaresearch head score (Gemma)0.016
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.016
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)0.0020.000

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 head0.079
GPT teacher head0.414
Teacher spread0.335 · 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
Published2023
Admission routes1
Has abstractyes

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