The Relationship of Alexithymia, Decision Making and Psychological Well-Being with Participation in Recreational Activities
Bibliographic record
Abstract
The aim of this study is to examine the relationship between alexithymia, decision making and psychological well-being with individuals' participation in recreational activities. The data used in the study were obtained from a total of 307 individuals who constituted the research group on the principle of volunteerism. The research group was formed on the basis of convenience sampling method. The research was conducted in the survey model. The data used in the study were obtained through Personal Information Form, Toronto Alexithymia Scale, Melbourne Decision Making Scale and Psychological Well-Being Scale. The internal consistency of the responses obtained within the scope of the research was determined by Crα coefficient. All analyses were conducted using the SPSS program. According to the findings it was determined that psychological well-being and careful decision-making style scores differed in favor of individuals who participated in recreational activities, gender and income level were not determinative in terms of the characteristics discussed, and single individuals who did not participate in recreational activities had significantly higher alexithymia scores. On the other hand, it was found, alexithymia had an inverse relationship with self-esteem in decision making, careful decision making and psychological well-being, and psychological well-being had a linear relationship with self-esteem in decision making and careful decision making. As a result, it can be said that participation in recreational activities may positively affect the level of careful decision-making tendency and psychological well-being.
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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 | 0.003 |
| 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 | 0.000 |
| 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".