Investigation of the Relationship Between the Alexithymic Characteristics of Secondary Students and Their Childhood Maltreatment Experiences
Bibliographic record
Abstract
ABSTRACT: This research is a descriptive study aiming at revealing the difference between alexithymia levels and childhood period trauma experience levels according to variables discussed, of students who study in different high schools. In the study, quantitative method and relational survey model have been used. Five hundred ninety-nine students (57.3% female, 42.7% male) studying in 7 different high schools located in a city in the Southeastern Anatolian Region of Turkey in 2018-2019 school year constitutes the study group of the research. A proper sampling method has been used to determine the study group. In the scope of the study, Personal Information Form, Toronto Alexithymia Scale, and Childhood Trauma Questionnaire-Short Form have been used as data collection tools. In data analysis, IBM SPSS 23 Pack software has been used. In the study, it has been observed that according to multivariate analysis of variance test, which is used with the purpose of determining the effects of independent variables, difficulty in expressing feelings and childhood trauma experiences that are the lower dimension of alexithymia become drastically distinct according to school type-class level joint interaction variable. It has been observed that levels of expressing feelings, physical neglect, and emotional neglect become distinct according to school type and childhood trauma experiences, school type-age joint interaction variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".