National Undergraduate Medical Core Curriculum in Turkey: Evaluation of Residents
Bibliographic record
Abstract
BACKGROUND: There is very little information available on self-perceived competence levels of junior medical doctors with regard to definitions by the National Core Curriculum (NCC) for Undergraduate Medical Education. AIMS: This study aims to determine the perceived level of competence of residents during undergraduate medical education within the context of the NCC. STUDY DESIGN: Descriptive study. METHODS: The survey was conducted between February 2010 and December 2011; the study population comprised 450 residents. Of this group, 318 (71%) participated in the study. Self-assessment questionnaires on competencies were distributed and residents were asked to assess their own competence in different domains by scoring them on a scale of 1 to 10. RESULTS: Nearly half of the residents reported insufficient experience of putting clinical skills into practice when they graduated. In the theoretical part of NCC, the lowest competency score was reported for health-care administration, while the determination of level of chlorine in water, delivering babies, and conducting forensic examinations had the lowest perceived levels of competency in the clinical skills domain. CONCLUSION: Residents reported low levels of perceived competency in skills they rarely performed outside the university hospital. They were much more confident in skills they performed during their medical education.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".