Facial emotion recognition ability: psychiatry nurses versus nurses from other departments
Bibliographic record
Abstract
PURPOSE: Facial emotion recognition is a basic element in non-verbal communication. Although some researchers have shown that recognizing facial expressions may be important in the interaction between doctors and patients, there are no studies concerning facial emotion recognition in nurses. Here, we aimed to investigate facial emotion recognition ability in nurses and compare the abilities between nurses from psychiatry and other departments. METHODS: In this cross-sectional study, sixty seven nurses were divided into two groups according to their departments: psychiatry (n=31); and, other departments (n=36). A Facial Emotion Recognition Test, constructed from a set of photographs from Ekman and Friesen's book "Pictures of Facial Affect", was administered to all participants. RESULTS: In whole group, the highest mean accuracy rate of recognizing facial emotion was the happy (99.14%) while the lowest accurately recognized facial expression was fear (47.71%). There were no significant differences between two groups among mean accuracy rates in recognizing happy, sad, fear, angry, surprised facial emotion expressions (for all, p>0.05). The ability of recognizing disgusted and neutral facial emotions tended to be better in other nurses than psychiatry nurses (p=0.052 and p=0.053, respectively) Conclusion: This study was the first that revealed indifference in the ability of FER between psychiatry nurses and non-psychiatry nurses. In medical education curricula throughout the world, no specific training program is scheduled for recognizing emotional cues of patients. We considered that improving the ability of recognizing facial emotion expression in medical stuff might be beneficial in reducing inappropriate patient-medical stuff interaction.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".