Transcultural adaptation and psychometric validation of the Thai-Brief Resilient Coping Scale: a cross-sectional study during the coronavirus disease 2019 pandemic in Thailand
Bibliographic record
Abstract
This study aimed to examine the transcultural adaptation, construct validity, and psychometric properties of the Thai-Brief Resilient Coping Scale (BRCS) among the general population and college students through the coronavirus disease 2019 (COVID-19) pandemic in Thailand. We invited the 4004 participants to complete sets of anchor-based measurement tools, including depressive symptoms, anxiety symptoms, perceived stress, well-being, and perceived social support. The scale factor structure of the Thai-BRCS was assessed using factor analysis, and nonparametric item response theory (IRT) analysis. The psychometric properties of the Thai-BRCS for validity (convergent and discriminant) and reliability (internal consistency and reproducibility) were assessed. Based on the construct validity testing, factor analysis, and nonparametric IRT analysis reaffirmed the unidimensionality with a one-factor structure of the Thai-BRCS version. For convergent validity, the scale was significantly correlated with all sets of anchor-based measurement tools (all P < 0.001). The discriminant validity was satisfactory with a group of medium and low resilience and the risk of adverse mental outcomes. For scale reliability, it revealed excellent internal consistency (alpha = 0.84, omega = 0.85) and reproducibility (intraclass correlation = 0.91). The Thai-BRCS version fulfills transcultural adaptation with satisfactory psychometric properties to measure psychological resilience in the Thai population during the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".