Examining the presence of alexithymic characteristics on an individual???s willingness to seek mental health counseling
Bibliographic record
Abstract
The purpose of this study was to understand how alexithymic characteristics relate to help seeking behavior.Additionally, this study sought to measure the levels of alexithymic characteristics between men and women and how they related to help seeking behavior in relation to mental health services.The Toronto Alexithymia Scale (TAS-20) was used to measure alexithymic characteristics and the Inventory of Attitudes toward Seeking Mental Health Services (IASMHS) was used to measure help seeking behavior in relation to mental health services.For this study, 65 individuals (15 men and 50 women) participated.The results show that higher alexithymic characteristics relate to lower scores in help seeking behavior.In this study, no statistically significant difference was found between men and women in their IASMHS scores.Additionally, no significant interaction was found between gender and alexithymic characteristics on the IASMHS.In sum, this means that when people report higher alexithymic scores, they have a lower likelihood of seeking mental health services.This study tries to provide information to counselors as to what might be one of the resons people with alexithymic chaeacteristics do not seek counseling or stay in counseling.I would like to start by dedicating this thesis to both of my mothers, Blanca and Geronima; it is thanks to your unconditional support that I have reached my dream.I would also like to thank my brother, Oliver, for helping me through the rough times and being my best friend.We have gone through this journey together and it was not easy, but it was only possible because you were there with me.I guess what I am trying to say is, gracias por creer en mí.I want to thank my thesis committee members: Dr. Emily Sommerman, Sheri Whitt, Dr. Lizabeth Eckerd, and Dr. Gregg Gold.Thank you for your help and support through these two years.Your feedback and guidance kept me going forward and helped during those hard times.I would like to thank Dr. William Reynolds for being my mentor all these years and for helping me start this thesis.Your research classes made this thesis a bit more manageable, thank you.I would also like to thank all my professors for providing some guidance and sharing their knowledge with me.These past years have been truly amazing and it was thanks to having met all of you.Lastly, I would like to
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 | 0.000 | 0.002 |
| 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".