Ruh sağlığı çalışanlarının yüz ifadelerinden duyguları ve karmaşık zihin durumlarını tanıma becerileri üzerine bir araştırma
Bibliographic record
Abstract
The main purpose of this study is to examine the ability of mental health workers to recognize emotions and complex states from facial expressions. For this purpose, 36 mental health professionals consisting of psychologists and psychological counselors and 24 people who are not mental health professionals were reached. The participants' ability to recognize emotions and complex states from facial expressions was assessed using the McGill Face Database. Since the original language of the data set was English, translation into Turkish and pilot study were done. The first hypothesis of this study was that mental health professionals recognize emotions and complex states from facial expressions better than other people. In the second hypothesis, it was expected that there is a significant difference between male and female participants in terms of facial expression recognition performance. The facial expression recognition performance of mental health professionals and the other group was analyzed by Independent Sample t-test, and it was found that mental health professionals recognized facial expressions significantly better. The face recognition performances of female and male participants were analyzed by Independent Sample t-test and no significant difference was found between the two groups. The findings of the research, contributions to the literature, limitations of the study and suggestions for future studies are discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".