An investigation of the relationship between alexithymia and empathy tendency in university students receiving health education
Bibliographic record
Abstract
PURPOSE: This study aims to identify the relationship between alexithymia and empathy tendency in university students receiving health education. DESIGN AND METHODS: This descriptive study was conducted with the volunteer participation of 376 students at a unıversity located in the eastern part of Turkey between September and October 2019. Data were collected through the Sociodemographic Form, the Empathy Tendency Scale, and the Toronto Alexithymia Scale. FINDINGS: The Empathy Tendency Scale was found to be significantly higher in females, in those who lived alone, who did not experience a traumatic event when they were children, whose mother was alive, and who were not subject to violence. As to the Alexithymia Scale, mean scores were significantly higher in males, in those whose mother or father was illiterate, who had a monthly family income of less than 2000₺, who graduated from a Religious Vocational High School, and whose mother was not alive (P %3C .05). A negative, significant relationship was found between the Empathy Tendency Scale mean score and the Toronto Alexithymia Scale (P %3C .001). PRACTICE IMPLICATIONS: A negative, significant relationship was found between empathy tendency and alexithymia. It is recommended that similar studies should be conducted in different regions as well.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".