The Effect of Emotion Management Training Given to Nursing Students on Alexithymia and Self- Consciousness: A Randomized Controlled Study
Bibliographic record
Abstract
Aim: This study was conducted to evaluate the effect of emotion management training given to nursing students on alexithymia and self-consciousness levels. Methods: This research, is control group intervention study. The sample of the study was determined by power analysis and 32 students were assigned to the intervention group and 32 students to the control group by simple random sampling method. The data were collected between February-June 2021. In the collecting data were used Socio-demographic Data Form, Toronto Alexithymia Scale and Self- Consciousness Scale. In the analysing of data were used t test for dependent groups, t test for independent groups, chi-square and ANOVA tests. Findings: In the intervention group of students’ Toronto Alexithymia Scale mean score pre-test are 67.03±10.86, post-test 49.75±8.25; in the control group of students pre-test are 69.03±8.23, post-test 69.87±10.15, and it was determined that the difference between the post-test mean scores of the groups was statistically significant (p<0.05). In the intervention group of students’ Self-Consciousness Scale mean score pre-test are 31.59±6.75, post-test 38.84±3.41; in the control group of students pre-test are 32.46±6.97, post-test 32.21±7.36, and it was determined that the difference between the post-test mean scores of the groups was statistically significant (p<0.05). Result: It was determined that given to students emotional management training were affected positive way alexithymia and self-consciousness levels.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".