Selecting emergency medicine: rationales from perspective of Iranian residents.
Bibliographic record
Abstract
Emergency medicine is a relatively new specialty in Iran. Therefore, the general public and the medical community do not have enough information on its duties, capabilities, its nature, and its work schedule or its degree of occupational difficulty compared to other specialties. Hence, an insight from the early group of residents who selected this specialty can help identify the strengths and weaknesses of this field in order to promote the scientific quality of this field, and attract medical students. It can also help to alleviate deficiencies and strengthen positive aspects of emergency medicine. The aim of this study was to identify the reasons behind choosing emergency medicine as a specialty. A qualitative study was conducted using semi-structured interviews. Maximum variation opportunistic sampling was done, and face-to-face interviews were held with 23 emergency medicine residents and fellows (4 faculty members and 19 residents). Data were analyzed through thematic analysis, and categories and themes were extracted. The main levels extracted were: 1) Individual priorities, 2) the nature of work and the field of study, and 3) professional future. The themes of each main level were extracted and encoded. This study showed that the majority of residents choose emergency medicine specialty to achieve a better social and professional status in one of the most challenging fields of medicine.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".