Prevalence and Correlates of Likely Anxiety Disorder in Ghana During the COVID-19 Pandemic: Evidence From a Cross-Sectional Online Survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic created stressors to daily living, leading to increased mental health problems. It is important to assess the influence of COVID-19 pandemic on mental health, specifically anxiety. OBJECTIVES: The goal was to determine the prevalence and sociodemographic, clinical, and other correlates of likely Generalized Anxiety Disorder (GAD) among study subjects in Ghana. DESIGN: This study employed a cross-sectional approach, using an online survey administered primarily through social media platforms. The survey questions included the GAD-7 scale, which was used to assess likely GAD in respondents. Data were analyzed using descriptive statistics, chi-square tests, and logistic regression analysis. PARTICIPANTS: Overall, 756 respondents completed the survey, mainly from Ashanti and Greater Accra, which were the hardest hit by COVID-19. RESULTS: The prevalence of likely GAD in our sample was 7.6%. Gender, loss of job due to COVID-19, and seeking mental health counseling were independently associated with increased likelihood of GAD. CONCLUSIONS: The findings suggest that women, those who lost their jobs due to the COVID-19 pandemic, and those who sought mental health counseling were more likely to experience moderate to high anxiety symptoms as a result of the COVID-19 pandemic. Priority must be attached to psychological support measures for members of these groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".