Re-testing the psychometric properties of the Fear of COVID-19 Scale (FCV-19s)
Bibliographic record
Abstract
The COVID-19 pandemic has brought about multi-faceted effects, including deleterious physical and mental health concerns. Fear of COVID-19 appears to result in varying mental and psychological symptoms among those in contact to patients with the disease. The study aimed to test the psychometric properties of the Fear of COVID-19 Scale (FCV-19s) in accurately measuring fear brought by the pandemic. One-hundred nineteen (119) hospital staff who were nurses currently working in private and/or public hospitals in Pampanga, who has had previous and/or current contact with COVID-19 patients, and are in optimal mental health state, were purposefully selected to participate in this study. Methodological research was employed to statistically test the psychometric properties of FCV-19s using exploratory factor analysis to test factor structure; Cronbach’s alpha to test internal reliability; and Pearson r with the Depression, Anxiety, and Stress Scale (DASS-21) to test concurrent validity. Statistical tests of the FCV-19s showed a single-factor structure, a Cronbach’s alpha score of 0.884, and a moderate correlation with DASS-21 (0.428). The present study was able to confirm the unidimensional construct, reliability, and concurrent validity of FCV-19s. Results contribute to the body of knowledge in terms of the accurate assessment of fear brought by the COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".