Determination of Alexithymia and Communication Skills Levels of Nursing Students After Restrictions in the Covid-19 Pandemic
Bibliographic record
Abstract
Objective: This descriptive cross-sectional study was carried out to determine nursing students’ alexithymia and communication skills levels and affecting factors, and to investigate the relationship between alexithymia level and communication skills after the restrictions of the Covid-19 pandemic.Materials and Methods: The sample consisted of nursing students (N=140) from a private university in Istanbul, Turkey. Data were collected using a Personal Information Form, Toronto Alexithymia Scale (TAS-20) and Communication Skills Scale (CSS).Results: It was determined that 90.7% (n=127) of the participants were female, the mean age was 20.63±1.53 and 30.7% (n=43) were senior nursing. 55.0% (n=77) of the participants stated that their interpersonal relations were good after the restrictions. Participants had a mean TAS-20 score of 55.15±9.12 and a CSS score of 102.25±11.29. It was determined that TAS-20 and CSS total and sub-dimension mean scores differed according to sociodemographic characteristics. The results showed that there was a significant relationship between the sub-dimension mean scores of both scales and the level of interpersonal relationships stated by the participants after the restrictions (p<0.05).Conclusion: It was stated that nursing students had moderate alexithymia and good communication skills. Participants who evaluated their interpersonal communication as bad after the restrictions had more difficulty in recognizing, expressing and communicating their emotions. It is recommended that innovative practices on emotion awareness and communication be integrated into the nursing curriculum, and that evidence-based research be conducted on the subject.
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 | 0.001 |
| 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".