Psychometric Properties of the Multidimensional Assessment of Covid-19-Related Fears (MAC-RF) in French-Speaking Healthcare Professionals and Community Adults
Bibliographic record
Abstract
The Multidimensional Assessment of COVID-19-Related Fears (MAC-RF) is an 8-item self-report measure, which is based on the theoretical premise that fear responses to COVID-19 involve different yet intertwined domains (i.e., bodily, relational, cognitive, and behavioural). In this multi-step study, we tested the psychometric properties of the French version of the MAC-RF and examined the reciprocal relationships among COVID-19-related fears. Data were collected in two French-speaking samples (N = 521 individuals from the community and N = 328 healthcare professionals). Internal reliability, convergent validity, construct validity, and internal structure of the MAC-RF were tested. The French version of the MAC-RF demonstrated good psychometric properties and a two-factor structure, with bodily and relational fears tapping into the first factor, and cognitive and behavioural fears tapping into the second factor. Healthcare professionals reported greater COVID-19-related fears than community participants. Correlation network analysis showed that fear for one’s own body and fear of taking action might increase the risk of experiencing other COVID-19-related fears. Limitations comprised the cross-sectional design of the study, risk of bias associated with self-report instruments, and use of online surveys. A careful assessment of different types of fear related to COVID-19 may have implications for prevention and clinical practice during the current coronavirus pandemic. The French version of the MAC-RF is valid and reliable and can thus be used for this purpose.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".