Validation of the Canadian French version of the fear of COVID‐19 scale in the general population of Quebec
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to develop a Canadian French translation of the fear of COVID-19 scale (FCV-19S) and assess its psychometric characteristics. METHODS: A forward and backtranslation process was conducted for the Canadian French version of the FCV-19S. The guidance of the ISPOR task force for translation and cultural adaptation was followed and cognitive debriefing interviews were conducted with six citizens. The final proofread Canadian French FCV-19S was then administered to a large sample of citizens from the province of Quebec in Canada through an online survey. A quota sampling was conducted in 2020. Respondents from the survey also completed the Clinical Outcomes in Routine Evaluation (CORE)-6D and the Sense of Coherence (SOC-3) questionnaires. Several psychometric tests were performed to investigate the reliability (internal consistency) and validity of the Canadian French FCV-19S, including construct validity, concurrent validity, and Rasch analysis. RESULTS: The translation process was conducted without any major difficulties. The cognitive debriefing interviews led to no change in the reconciled translation. The survey collected answers from 3428 citizens. Results indicated that the factor structure of the Canadian French FCV-19S is a unidimensional factor fitting well with the data. The scale showed adequate reliability (Cronbach's alpha of .903) and concurrent validity, as indicated by significantly negative correlation with CORE-6D (r = -.410) and SOC-3 (r = -.233). The Canadian French FCV-19S properties tested using Rasch analysis was also very satisfactory. CONCLUSIONS: The results of the present study indicated that the Canadian French version of FCV-19S is a unidimensional tool with robust psychometric properties in the adult's population of all ages residing in the province of Quebec, Canada.
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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 | 0.007 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".