Translation of the Fear of COVID-19 Scale into French-Canadian and English-Canadian and Validation in the Nursing Staff of Quebec
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, Quebec has been one of the most affected provinces in Canada. Rising fear of COVID-19 is inevitable among healthcare workers, and a new scale was developed to measure this type of fear, the Fear of COVID-19 Scale (FCV-19S). Aims: To translate the FCV-19S into French-Canadian and English-Canadian, and to validate both versions in the nursing staff from Quebec. Methods: A cross-sectional online survey was sent to approximately 15 000 nursing staff including nurses and licensed practical nurses among those who had consented to their respective Order to be contacted for research. The forward-backward method was used to translate the FCV-19S into French-Canadian and English-Canadian. Both versions along with stress and work-related questionnaires, were used to establish validity. Results: A total of 1708 nursing staff, with a majority of women, completed the survey (1517 and 191 completed the French-Canadian and English-Canadian versions). A unidimensional scale was confirmed for both versions with Cronbach alphas of 0.90 and 0.88. Discriminative values showed higher fear levels in women, and in generation X (40-56 years old). Higher fear levels were also found in nursing staff working in long-term care facilities, provided care to COVID-19 patients who died, and those who felt less prepared to provide safe care. Convergent associations were found between fear levels, stress, work satisfaction, and turnover intention. Discussion and conclusion: A rigorous approach was used to translate the fear of COVID-19 scale into French-Canadian and English-Canadian. Both Canadian versions of the FCV-19S supported a valid unidimensional scale in Quebec nursing staff.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".