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Record W3042417920 · doi:10.1016/j.recesp.2019.12.029

Guía ESC 2019 sobre el tratamiento de pacientes con taquicardia supraventricular

2020· article· es· W3042417920 on OpenAlexaboutno aff

Bibliographic record

VenueRevista Española de Cardiología · 2020
Typearticle
Languagees
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleCurriculumMedical educationMedicinePsychologyFamily medicineNursingPedagogy

Abstract

fetched live from OpenAlex

Research sets the foundation for developing plastic surgeons who think critically and approach clinical practice with an inquisitive mind. The objective of this study was to characterize current attitudes and perceived barriers towards conducting research during residency.A validated 36-item questionnaire was developed by a national task-force of Canadian plastic surgery trainees. The survey was distributed to all 13 plastic surgery programs in Canada. Data was collected for a period of 2 months in the form of multiple choice, Likert scales and short answers.The response rate was 64% (95/149) with representation from all 13 plastic surgery programs across Canada. The top three perceived barriers to conducting research were lack of time (83%), insufficient access to research supervisors and mentors (42%) and the research ethics process (38%). More than 70% of residents were interested in conducting research during residency and 74% of programs have a research requirement integrated into their curriculum. Despite this, less than half of residents (47%) believed that their program fosters a culture that promotes research. This was attributed to multiple factors, including a lack of internal research funding (78%), limited access to a research methods or clinical trials unit (78%), and insufficient research training (68%). University research ranking had no correlation with residents’ scholarly output or their perceptions towards research barriers.Canadian Plastic Surgery residents identified several important factors considered to be barriers to research. Programs can use these findings to address barriers and improve the integration of research throughout residency training.

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)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.056
GPT teacher head0.367
Teacher spread0.311 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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