The Relatinship Between Personality Traits and Coping Strategies in the Alexithymia Prediction of Chronic Obstructive Pulmonary Patients Referred to Valiasr Hospital in Fasa in 2016
Bibliographic record
Abstract
Background & Objective: The purpose of this study was to investigate the relationship between personality traits and coping strategies in the alexithymia prediction of chronic obstructive pulmonary patients referred to Valiasr Hospital in Fasa, Iran. Materials & Methods: The statistical population of this study included all patients referring to the specialized clinic of Vali Asr Hospital in Fasa, which were selected by random sampling method of 180 people based on Cochran sample size formula. In this study, three Alexey Times Toronto Questionnaires (FTAS-20), Neo-Personality Traits Questionnaire (NEO-IP-R), and Lazarus and Folkman Coping Strategies were used, Data were analyzed using SPSS software and correlation method and stepwise regression analysis were used. Results: The results showed that there was a significant correlation between neuroticism, extroversion and agreeableness personality traits and emotional coping strategies(P =0.000). However, no significant relationship was found between personality trait openness and conscientiousness and coping strategies. Also, the excitement style (P = 0.745; Beta = 0.745), extroversion (P = 0.331, Beta = 0.300), neuroticism (P =.0000 Beta =.288) and agreement P = 0.098 Beta =.098) can predict Alexithymia in patients with Chronic Obstructive Pulmonary Disease (COPD). But age, gender, and education were not good predictors of the Alexei time in Copd patients. Conclusion: The results of this study showed that the personality factors of neuroticism, extraversion and consensus-seeking can predict the changes in Alexis time in both positive and negative directions, and the anti-emotional coping style is also negatively predicted by Alexei Time.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".