COVID-19 and Activities of Daily Living Among Primary Health Care Workers in Ekiti State, South-West Nigeria.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Globally, COVID-19 has greatly impacted humans physically, socially, mentally, and economically. No doubt, healthcare workers seemed to bear the greatest impact. The study therefore assessed the impact of COVID- 19 on the primary healthcare workers' daily activities in Ekiti, Southwest, Nigeria. METHODS: The study was a cross-sectional study using a quantitative data collection method among 716 primary healthcare workers. Respondents were selected using an online convenience sampling method via their social media platforms. Data was collected, collated, and analyzed using SPSS version 25 software and presented as frequency tables, mean and standard deviation. Bivariate/multivariate analyses were conducted using t-tests and ANOVA statistics. The level of statistical significance was set at p<0.05. RESULTS: The mean age of respondents was 44.4+6.4SD with less than half (47.1%) between 41-50 years age group. The majority of the respondents (89.4%) were female and almost all (96.2%) were married. Ninety percent (90%) had ever heard of Coronavirus and (85.8%) had to spend more money on activities of daily living such as transportation (90.1%), groceries (80.6%), assisting relations (95.8%) and sanitary measures (disinfection) at home (95.0%). COVID-19 had a huge negative impact on the majority (89.7%) of healthcare workers with a mean score of 22+4.8. CONCLUSION: COVID-19 negatively impacted the daily living and professional duties of primary healthcare workers which reflected in their psychological, physical, social and economic well-being. Disease outbreaks are unlikely to disappear soon, hence, global proactive interventions and homegrown measures should be adopted to protect healthcare workers and save their lives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".