Prevalence and changes in boredom, anxiety and well-being among Ghanaians during the COVID-19 pandemic: a population-based study
Bibliographic record
Abstract
BACKGROUND: The outbreak of the COVID-19 pandemic has been associated with several adverse health outcomes. However, few studies in sub-Saharan Africa have examined its deleterious consequences on mental health. Therefore, we investigated the prevalence and changes in boredom, anxiety and psychological well-being before and during the COVID-19 pandemic in Ghana. METHODS: Data for this study were drawn from an online survey of 811 participants that collected retrospective information on mental health measures including symptoms of generalized anxiety disorder, boredom, and well-being. Additional data were collected on COVID-19 related measures, biosocial (e.g. age and sex) and sociocultural factors (e.g., education, occupation, marital status). Following descriptive and psychometric evaluation of measures used, multiple linear regression was used to assess the relationships between predictor variables and boredom, anxiety and psychological well-being scores during the pandemic. Second, we assessed the effect of anxiety on psychological well-being. Next, we assessed predictors of the changes in boredom, anxiety, and well-being. RESULTS: Before the COVID-19 pandemic, 63.5% reported better well-being, 11.6% symptoms of anxiety, and 29.6% symptoms of boredom. Comparing experiences before and during the pandemic, there was an increase in boredom and anxiety symptomatology, and a decrease in well-being mean scores. The adjusted model shows participants with existing medical conditions had higher scores on boredom (ß = 1.76, p < .001) and anxiety (ß = 1.83, p < .01). In a separate model, anxiety scores before the pandemic (ß = -0.25, p < .01) and having prior medical conditions (ß = -1.53, p < .001) were associated with decreased psychological well-being scores during the pandemic. In the change model, having a prior medical condition was associated with an increasing change in boredom, anxiety, and well-being. Older age was associated with decreasing changes in boredom and well-being scores. CONCLUSIONS: This study is the first in Ghana to provide evidence of the changes in boredom, anxiety and psychological well-being during the COVID-19 pandemic. The findings underscore the need for the inclusion of mental health interventions as part of the current pandemic control protocol and public health preparedness towards infectious disease outbreaks.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".