COVID Stress Scales: A Cross-sectional Study of its Psychometric Properties among Africans with Chronic, Stable Medical Conditions
Bibliographic record
Abstract
Background: The coronavirus 2019 pandemic (COVID-19) elicited in various populations, diverse psycho-behavioral responses. The COVID Stress Scales (CSS) was developed and validated in the general population, among US and Canadian adults, in response to the COVID-19 pandemic. Expectedly, population-specific variations in response are likely, hence, the need to validate psychometrically sound instruments across cultures and diverse populations. Aim: To determine the factor structure and reliability estimates of the CSS among participants with chronic, stable medical conditions in a Nigerian Tertiary Hospital. Methods: The cross-sectional study, involved 1047 consenting adults with chronic, stable medical conditions attending the out-patient specialist clinics of a foremost tertiary hospital in Nigeria. The participants were enrolled consecutively over a four-month period straddling the first and second waves of the COVID-19 pandemic. The CSS was administered to participants who fulfilled the study criteria. Exploratory Factor Analyses (EFA) using Principal Component Analysis and Oblimin rotation with Kaiser Normalization, was used to extract the factors. Results: A six-factor structure emerged: COVID-19-related socio-economic consequences; contamination; xenophobia; traumatic stress; compulsive checking and reassurance seeking; and danger. The internal consistency within items of each domain was acceptable (Cronbach alpha 0.85 and above) and correlation between the domains was moderate to strong. Conclusion: The CSS maintained a six-factor structure, corresponding to the six scales, among Nigerian participants with chronic, stable medical conditions. It has acceptable reliability estimates and can be used to assess COVID-19-related anxiety in this population. The inter-correlation of the various domains is a strong evidence for the existence of COVID stress syndrome.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".