Brazilian Dental Students’ Perceptions About Medical Emergencies: A Qualitative Exploratory Study
Bibliographic record
Abstract
Dental students have little understanding about medical emergencies, and there is very little in-depth data about the importance they place on this important area that is fundamental to their professional training. This study aimed to identify the perceptions of a group of undergraduate dental students about the dentistry-medical emergency interface. Twenty undergraduate dental students at the Federal University of Goias, Brazil, took part in this study. The data were collected through in-depth interviews with these students and were interpreted using qualitative content analysis. Two themes emerged from this data analysis: dentistry as a comprehensive health science, and students' knowledge, feelings, and attitudes about medical emergencies in the dental office. Based on the students' perceptions, an interface between dentistry and medical emergencies in the dental office was proposed that is comprised of the following intertwined concepts: 1) dentistry is a health science profession that should focus on the whole patient instead of being limited to the oral cavity; 2) medical emergencies do occur in the dental office, but students' minimal knowledge about these incidents and their etiology causes feelings of insecurity, dissatisfaction, and a limited appreciation of the dentists' responsibility; and 3) the inability to perform proper basic life support (BLS) technique in the dental office is the ultimate consequence. Undergraduate health courses need to develop strategies to teach professionals and students appropriate behavior and attitudes when facing life-threatening emergencies.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".