Examining whether Self-esteem and traits of Alexithymia influence university student’s attitudes towards help-seeking
Bibliographic record
Abstract
Objective: The current study aimed to examine if self-esteem and traits of alexithymia influenced individual’s attitude towards help-seeking. College students are of interest due to the transition they make from school to college as this can be a difficult time (adjustment wise). There is research on positive self-esteem, gender differences towards attitudes and alexithymic individuals having higher levels of depression and psychological suffering. Therefore, to investigate these variables and discover what we can do to aid individuals would be of importance. Methods: The study consisted of 108 college (part-time and full-time students) participants, with 33 males and 75 females. The participants were asked demographic questions and were measured on three scales; the Rosenberg Self-esteem scale, the Toronto Alexithymia Scale (TAS-20) and the Inventory of Attitudes Toward Seeking Mental Health Services scale (IASMHS). Results: Alexithymic traits were the strongest predictor of attitudes towards help-seeking. Males had a more positive attitude towards help-seeking then females and individual’s scores going down from high self-esteem to medium self-esteem had more positive attitudes towards help-seeking. Conclusion: Findings from this study indicate that individuals with alexithymic traits present are having a more positive attitude towards help-seeking over those with none and that alexithymia is a strong predictor of attitudes. It also indicates that males are more positive over females in attitudes. These results should be further examined as they play a major role in why people would be more inclined to help-seek or not.
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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 | 0.000 | 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) | 0.004 | 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".