COVID-19 Stres Ölçeğinin Türkçe Formu: Geçerlik ve güvenirlik çalışması
Bibliographic record
Abstract
During the COVID-19 pandemic, some measurement tools have been developed to assess pandemic-related stress. One of these measurement tools is the COVID-19 Stress Scale, which can evaluate the stress related to this disease in a versatile way. In the current study, it was aimed to adapt the COVID-19 Stress Scale into Turkish. In total, 180 people, whose ages ranged from 18 to 60, participated in the study. To examine the construct validity of the scale, exploratory (EFA) and confirmatory (CFA) factor analyses were carried out, and for criterion validity, correlation with similar scales (COVID-19 Fear Scale, Coronavirus Anxiety Scale, Warwick Edinburgh Mental Well-Being Scale, and Vancouver Obsessive Compulsive Inventory) was checked. As a result of the analysis, it is noteworthy that the scale, which was originally a five-factor structure, has a six-factor structure (threat/danger, socioeconomic consequences, xenophobia, contamination, traumatic stress, and compulsive control) in our country. Also, it was determined that item factor loads ranged between .35 and .90, and the criterion-related validity was at an acceptable level. The scale is associated with similar measurement tools. According to the internal consistency coefficient calculated to evaluate the reliability of the scale, the Cronbach's alpha reliability coefficient for the whole scale is .97, while these values vary between .89 and .96 for the sub-dimensions. Item-total correlations are between .63 and .82. In addition, split-half levels of the scale were calculated as .86. These findings support that the Turkish version of the scale is a reliable and valid measurement tool in assessing the stress due to COVID-19. The scale will make important contributions to the related literature in terms of allowing multi-dimensional evaluation on the basis of different sources of anxiety and showing good psychometric properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".