The Effects of Dichotomous Thinking on Depression in Japanese College Students
Bibliographic record
Abstract
The purpose of this study was to explore the effect of dichotomous thinking on depression. We attempted to test the following hypotheses: 1) dichotomous thinking increases depression, and 2) dichotomous thinking has two routes to increase depression—direct, associative processing, and indirect, reflective processing. Two hundred Japanese college students (Males: 107, Females: 93, M age= 20.02 ± 1.42) were asked to complete the Dichotomous Thinking Inventory, which consists of three subscales: dichotomous belief, profit-and-loss thinking, and preference for dichotomy; the Kessler 6 Distress Scale; and the Japanese version of the Rumination-reflection Questionnaire. We conducted structural equation modelling to test the hypotheses. The results supported the hypotheses and indicated that dichotomous thinking increased depression. There were two different routes: dichotomous belief directly increased depression and profit-and-loss thinking indirectly increased depression by way of rumination. There are some implications of the findings. This study suggests that cognitive distortions might causes depression from two paths and practical interventions might also have two different routes or approaches to depression.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".