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Record W2942262204

Aprendizaje basado en problemas como estrategia didáctica en Odontología y su especialización de Ortodoncia

2018· dissertation· es· W2942262204 on OpenAlexaboutno aff
M.R. Mourelle Martinez, Giovanni Eduardo

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldHealth Professions
TopicHealth and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)CurriculumDocumentary evidenceSpecialtyMedical educationProblem-based learningPedagogyPsychologyMedicineHumanitiesPolitical scienceArtFamily medicinePolitics
DOInot available

Abstract

fetched live from OpenAlex

Summary Teaching and learning in any profession require the use of one or several didactics that effectively lead to the formation of students and this is not foreign to dentistry and more specifically to the specialty of orthodontics. Problem-based learning (PBL) as a teaching tool is more than 40 years since its inception at the University of Mc Master and is sufficiently demonstrated its effectiveness in improving the quality of learning in students. In Colombia there is little or no documentary evidence about the application of PBL in the training of dentists and their specialties, this essay aims from the training in University Teaching and literary review open a space for the initiation of research and subsequent promotion of the use of the ABP in the study of Dentistry in our country. The methodology used in this essay is of qualitative descriptive documentary review, a description of PBL implementation experiences in different countries such as Germany, Canada, United States, England, China, Japan, Malaysia, Saudi Arabia among others. The results of this review made it possible to establish that the PBL is a didactic tool that is frequently used around the world in the curricula of dental schools of prestigious universities such as Harvard or Mac Master, it was also found that the literature focuses to a large extent on the perception of students against PBL and how motivation plays an important role in its implementation.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient 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: none
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0230.005

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 head0.045
GPT teacher head0.455
Teacher spread0.411 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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