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Record W2941859176

[Internal Medicine in the curriculum of General Medicine at Universities of Mexico, 2014].

2018· article· en· W2941859176 on OpenAlexaboutno aff
Jesús Adrián Maldonado, José María Peinado

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipCurriculumSubject (documents)Medical educationQuarter (Canadian coin)MedicineLikert scalePsychologyPedagogyLibrary scienceComputer scienceHistory
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to analyze Internal Medicine as a subject and its requirement in each of the Universities curriculum in Mexico that offers a degree in General Medicine. By the end of the first quarter of 2014, the research was closed and 81 campuses were studied. This research was quantitative, using an analytical technique, written discourse, exploratory and purposive sampling not random and homogeneous type. The Likert questionnaire was used in this study to analyse the following variables: the record of Internal Medicine as a subject, the burden of credit, and the location of the program. The procedure consisted of three phases. First obtaining an official list of all the Universities in the Mexican Association of Colleges and Schools of Medicine. Second, obtaining an analysis of each of the Universities' curriculums, and lastly gathering each variable of the study. The results of the Universities were 63% were public and 37% private. Internal Medicine as a subject in the curriculum was 37.1%, and 20% of the universities include it for six months and 9% offer it the whole year. However, the undergraduate internship in Internal Medicine offers it 100%. In conclusion, Internal Medicine as a subject could disappear from the curriculum in General Medicine before coming to the undergraduate internship, even though the latter is declared required in hospital shifts.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: yes
Not applicablelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.415
Teacher spread0.360 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Not applicable
Domainnot available
GenreEmpirical · Other

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
Published2018
Admission routes1
Has abstractyes

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