Validation of the Canadian English and French Versions of the Fear of COVID-19 Scale in Quebec Nursing Staff
Bibliographic record
Abstract
Nursing staff have been at the forefront of the pandemic, reporting high traumatic stress and anxiety levels related to high fear of COVID-19. Recommendations from previous studies include using the Fear of COVID-19 Scale (FCV-19S) as a screening tool to identify any individuals who may benefit from targeted psychological support. Thus far, the accuracy of the Canadian English and French versions of FCV-19S to detect high levels of traumatic stress and anxiety symptoms has not been examined. The objectives of this methodological psychometric study were to examine among nursing staff: (a) the structure and internal consistency of the Canadian versions of the FCV-19S and (b) its ability in detecting high levels of traumatic stress and anxiety symptoms. An anonymous online survey was distributed among nursing staff (n = 387) in the province of Quebec (Canada). This survey included the FCV-19S and scales measuring their traumatic stress (PCL-5) and anxiety symptoms (GAD-7). Exploratory factor analysis and receiver operating characteristic (ROC) analyses were performed. The one-factor structure of the FCV-19S was supported (Cronbach alpha = 0.87). The FCV-19S showed better accuracy for the detection of traumatic stress (area under the curve (AUC) 0.75 [95% CI 0.68, 0.82]) in comparison to anxiety symptoms (AUC 0.65 [95% CI 0.60, 0.74]). The FCV-19S may benefit from adaptation for its use in nursing staff and in a future pandemic context.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".