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Formación científica en el pregrado de medicina en Chile: ¿dónde estamos? y ¿hacia dónde vamos?

2020· article· es· W3099849961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRevista médica de Chile · 2020
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumTeamworkMedical educationBologna ProcessHumanitiesMedicineSociologyPolitical scienceHigher educationPedagogyPhilosophy

Abstract

fetched live from OpenAlex

For more than a century the training of medical professionals has been organized according to the Flexnerian model, which comprises three cycles: basic, clinical and clerkship. On the other hand, the accelerated development of biomedical sciences modified the competences of the first cycle. Additionally, new skills required for medical practice, such as teamwork and innovation as a tool to solve health problems, challenged in recent years the classic paradigm of medical education. Therefore, the medical schools have developed multiple strategies to deal with it, such as curricular integration using competency-based education models, incorporating basic and clinical sciences in parallel during the curriculum, ensuring a relevant and applicable scientific knowledge throughout the training process. Although in Chile the Flexner prototype is still followed, the basic sciences are taught as single or integrated courses or using a systems approach. In this article we report a diagnosis about the local integration of fundamental sciences in medical training. We also compare our schools with those of Canada, Europe and Latin America. Recommendations aimed at modernizing medical school curricula are made.

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.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.033
GPT teacher head0.422
Teacher spread0.389 · 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