Effects of Coronavirus Pandemic on Young Adults’ Ability to Access Health Services and Practice Recommended Preventive Measures
Bibliographic record
Abstract
Given the limited attention to young adults as key contributors to the spread of COVID-19 in Uganda, this study examines the effects of the outbreak on the ability of young adults aged 18-29 to access health services and practice preventive measures. A national population-based mobile phone survey was conducted in December 2020. Multivariable regression analyses were used to explore the effect of the COVID-19 pandemic on access to health care services. Control variables included region, education level, parity, and source of health information. The majority (98%) perceived COVID-19 as a serious threat to Ugandans. Although the majority reported handwashing (97%) and masking (92%), fewer respondents avoided shaking hands (39%), ensured physical distancing (57%), avoided groups of more than four people (43%), stayed home most days (30%), avoided touching eyes, nose, and mouth (14%), and practiced sneezing/coughing into their elbow (7%). Participants noted that the COVID-19 pandemic affected their ability to access family planning (40%), HIV (49%), maternal health (55%), child health (56%), and malaria (63%) services. The perceived effect of the COVID-19 pandemic on services was higher for those in the Northern region (OR= 2.00, 95% CI 1.00-4.02), those with higher education OR= 2.26, 95% CI 1.28-3.99), those with five plus children (OR= 2.05, 95% CI 0.92-4.56), and those who trust radio for COVID-19 information (OR= 1.65, 95% CI 1.01-2.67). The findings show the pragmatic importance of understanding the dynamic characteristics and behavioral patterns of young adults in the context of COVID-19 to inform targeted programming.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".