Schema Prevalence and Variation: A Study of 7500 Young Schema Questionnaires from Iranian Telegram Bot Users
Bibliographic record
Abstract
This study aimed to investigate the prevalence and variation of early maladaptive schemas across gender and age groups in a large sample of Iranian participants using data collected via a Telegram bot. The study employed a descriptive, cross-sectional design, analyzing data from 7,659 participants who completed the Young Schema Questionnaire-Short Form (YSQ-SF). The YSQ-SF assesses 18 maladaptive schemas, and the total count of maladaptive schemas was calculated for each participant. Data were collected anonymously through an automated bot on Telegram from 2014 to the present. Statistical analyses were performed using SPSS version 26, including descriptive statistics, independent t-tests, and one-way ANOVA to explore differences across gender and age groups. The analysis revealed that Emotional Deprivation, Abandonment/Instability, and Vulnerability to Harm or Illness were the most prevalent schemas among participants. Females reported significantly higher maladaptive schema counts than males (t = -3.26, p = 0.001). Age group comparisons indicated that the 0-18 age group had the highest mean maladaptive schema count (M = 6.26, SD = 3.61), followed by the 19-35 group (M = 4.83, SD = 3.67), with the lowest count observed in participants aged 55 and above (M = 3.98, SD = 3.56; F = 67.61, p < 0.001). The findings suggest a decline in maladaptive schemas with increasing age and significant gender-specific patterns. The study highlights the widespread prevalence of early maladaptive schemas in the Iranian population and underscores demographic variations influenced by gender and age. These findings have important implications for culturally tailored interventions, particularly for younger individuals and women who exhibit higher schema counts. Further research is needed to explore longitudinal and qualitative aspects of schema development in diverse populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".