The Fear of COVID-19 Familial Infection Scale: Initial Psychometric Examination
Bibliographic record
Abstract
Objective: The COVID-19 pandemic has not only a physical health impact but also a psychological toll, which is associated with the social isolation and emotional contagion of fear and anxiety. One of the main factors which influence the increased levels of stress is the fear of COVID-19, and specifically the fear of being infected, and of transmitting the virus to one’s family and friends. In this study, a new measure named “The Fear of COVID-19 Familial Infection Scale” (FCFI) is suggested, and its psychometric properties are tested. Methods: A sample of 582 participants filled an online survey; of those, 393 (67.5%) were healthcare workers. Of the healthcare workers, 218 (37.5%) were medical doctors, 46 (7.9%) were nurses, and 117 (20.1%) were other healthcare professionals. Participants filled out a demographic questionnaire, The FCFI, the Fear of the COVID-19 scale, and the Depression and Anxiety Scale (DASS-21). Results: Exploratory factor analysis revealed that the FCFI has two factors: Fear of infecting others, and Perception of Others’ fear of being infected by me. This bidimensional model accounts for 69.5% of the variance in the FCFI. The two subscales had good reliability and high convergence validity as indicated by its correlations with being exposed to COVID-19, fear of COVID-19 and the DASS-21 subscales. Conclusion: The FCFI has initial good psychometric properties and could be a useful tool to assess levels of fear of COVID-19 familial infection.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".