Validity and reliability of the Serbian COVID Stress Scales
Bibliographic record
Abstract
This study aimed to generate a linguistic equivalent of the COVID Stress Scales (CSS) in the Serbian language and examine its psychometric characteristics. Data were collected from September to December 2020 among the general population of three cities in Republic of Serbia and Republic of Srpska, countries where the Serbian language is spoken. Participants completed a socio-demographic questionnaire, followed by the CSS and Perceived Stress Scale (PSS). The CSS was validated using the standard methodology (i.e., forward and backward translations, pilot testing). The reliability of the Serbian CSS was assessed using Cronbach's alpha and McDonald's omega coefficients and convergent validity was evaluated by correlating the CSS with PSS. Confirmatory factor analysis was performed to examine the construct validity of the Serbian CSS. This study included 961 persons (52.8% males and 47.2% females). The Cronbach's alpha coefficient of the Serbian CSS was 0.964 and McDonald's omega was 0.964. The Serbian CSS with 36 items and a six-factorial structure showed a measurement model with a satisfactory fit for our population (CMIN/DF = 4.391; GFI = 0.991; RMSEA = 0.025). The CSS total and all domain scores significantly positively correlated with PSS total score. The Serbian version of the CSS is a valid and reliable questionnaire that can be used in assessing COVID-19-related distress experienced by Serbian speaking people during the COVID-19 pandemic as well as future epidemics and pandemics.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".