Prediction of tendency to addiction based on alexithymia and assertiveness in nursing students
Bibliographic record
Abstract
Background: Given the high prevalence of addiction, it is essential efforts to identify the risk factors for this problem in different populations. Therefore, the present study aimed to predict the tendency of addiction based on the amount of alexithymia and assertiveness in nursing students. Materials and Methods: The research design was correlational. The sample consisted of 160 nursing students of Kermanshah University of Medical Sciences and Kermanshah Azad University who were studying in the academic year of 2018-19 and were selected by cluster sampling. Data were collected using Weed and Butcher addiction potential scale, Toronto alexithymia scale, and Gambrill and Richey assertiveness questionnaire. Pearson correlation and multivariate regression analysis were used to analyze the data. Data were analyzed by SPSS-24 software. Results: The results of Pearson correlation coefficient showed that there is a positive significant relationship between alexithymia and with nursing students' tendency to addiction and there is a negative significant relationship between assertiveness and their tendency to Addiction (P<0.01). The results of multivariate regression analysis also revealed that 23% of the variance in active addiction preparation is explained by alexithymia and assertiveness (R2= 0.230). Also, 67.7% of the variance of passive addiction preparation is explained by alexithymia and assertiveness (R2= 0/67/7). Conclusion: According to the results of the study, it seems that two components of alexithymia and assertiveness with addiction preparation are related to addiction and cause nursing students to tend to addiction and need the attention of the educational system to reduce emotional arousal and improve assertiveness through holding workshops.
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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 | 0.003 |
| 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 | 0.001 | 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".