Web-Based Training Program Using Cognitive Behavioral Therapy to Enhance Cognitive Flexibility and Alleviate Psychological Distress Among Schoolteachers: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Schoolteachers are known to be faced with various stresses in their work. A simple, less onerous, and effective intervention technique that can enhance the stress management skills, particularly, cognitive flexibility, of schoolteachers is needed. OBJECTIVE: This study aimed to determine whether stress management training using a Web-based cognitive behavioral therapy (CBT) program is effective for enhancing the cognitive flexibility of schoolteachers and alleviating their subjective distress. METHODS: This study was conducted in a random controlled design covering public elementary schoolteachers. Teachers allocated to the intervention group received 120 min of group education and completed homework using a Web-based CBT program that lasted for 3 months. The items of outcome evaluation were cognitive flexibility and subjective distress, and the efficacy of intervention was evaluated at 3 months after intervention. RESULTS: A total of 240 participants were randomly allocated to the intervention group (120 individuals) and the control group (120 individuals). On the basis of the principle of intention to treat, the intervention group and the control group were compared regarding the amount of change from before intervention to after intervention, using a general linear model. Scores of cognitive flexibility and subjective distress were significantly more improved in the intervention group than in the control group. CONCLUSIONS: The results of this study suggest that simple stress management training using a Web-based CBT program in elementary schoolteachers enhances cognitive flexibility and alleviates subjective distress.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".