The Effectiveness of Emotion Regulation Training on Metacognitive Beliefs and Pain Perception in Patients with Functional Indigestion
Bibliographic record
Abstract
Background: The most common gastrointestinal disorders are functional gastrointestinal disorders (FGID) of which functional indigestion is one of the most common types and causes the deterioration of health and reduction of quality of life (QOL). This study was conducted with the aim to determine the effectiveness of emotion regulation training on metacognitive beliefs and pain perception in patients with functional indigestion. Methods: The present study was a quasi-experimental research with pretest-posttest design and a control group. The statistical population consisted of all patients with functional indigestion in Tehran, Iran, in 2020. The sample consisted of 30 patients who were selected through the convenience sampling method and randomly assigned to an experimental group (emotion regulation training) and a control group (each consisting of 15 people). The research tools included the Metacognitions Questionnaire (Wells & Cartwright-Hatton, 2004) and McGill Pain Questionnaire (Melzack, 1975). Data analysis was performed using analysis of variance in SPSS software. Results: The findings showed that emotion regulation training was effective on metacognitive beliefs (P < 0.001) and pain perception (P < 0.001) in patients with functional indigestion. Conclusion: It can be concluded that emotion regulation training was effective on metacognitive beliefs and pain perception in patients with functional indigestion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".