COVID-19 Peritraumatic Distress Index Instrument – Translation and Validation of Bulgarian Version
Bibliographic record
Abstract
BACKGROUND: COVID-19 peritraumatic distress index (CPDI) self-report questionnaire was designed to measure peritraumatic psychological distress in a pandemic emergency. AIM: The aim of the study was the validation of Bulgarian COVID-19 peritraumatic distress index (CPDI) questionnaire and its application to measure psychological distress level in Bulgarian population. METHODS: The study was conducted among 42 adults from February 2022 to March 2022. The average age of respondents participating in the validation of COVID-19 peritraumatic distress index (CPDI) questionnaire is 40.88 ± 13.309, women being predominant - 71.4% (n = 30), as well as individuals with higher education- 69% (n = 29). Following the preliminary instruction, all participants filled out the online Bulgarian version of the questionnaire anonymously 2 times within a period of 2 weeks. Data were analyzed using descriptive statistic, the Wilcoxon signed-rank test, Cronbach’s alpha, and Corrected Item-Total Correlation. RESULTS: The CPDI instrument was linguistically validated according to a standard procedure (8) and cross-culturally adapted (9) into Bulgarian in several stages. The overall Cronbach’s alpha for the Peritraumatic Distress Index (CPDI) questionnaire is 0.940. Almost all corrected item-total correlations exceeded the accepted cut off of 0.30 indicating each item was related to the overall scale except for Q5 “I feel sympathetic to COVID-19 patients and their families.” CONCLUSION: The Bulgarian version of the questionnaire reveals good reliability and cross-cultural validity and can be applied widely for measuring the prevalence of psychological suffering and distress in the pandemic emergency.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".