Health Behavior and Older Adults’ Experiences Using Health Services During the COVID-19 Pandemic in Rural Nigeria: A Qualitative Study Using the Socio-Ecological Model of Health Behavior
Bibliographic record
Abstract
A bstract Background: Older adults are disproportionately impacted during public health crises, especially in rural settings where healthcare access is limited. Understanding their health behaviors and healthcare utilization is essential for improving geriatric care. Objectives: This study investigates the understanding of health behavior and the healthcare experiences of older adults using outpatient services in a rural Nigerian hospital during the COVID-19 pandemic. Materials and Methods: The study employed an exploratory qualitative research design through 13 face-to-face semi-structured interviews with older adults aged 60–80 using outpatient health services in a University Teaching Hospital in rural south-eastern Nigeria. The data generated from these interviews were then analyzed using qualitative content analysis, which led to two overarching themes. Results: Our findings revealed a complex interplay of personal, interpersonal, societal, and environmental factors that influence health behavior and healthcare utilization among older adults in rural Nigeria during the COVID-19 pandemic. While negative behaviors, such as heightened emotions, poor help-seeking behavior, self-medication for COVID-19 symptoms, and poor diets, were observed due to ignorance and misinformation, there were also positive influences. These included an increased focus on hygiene and enhanced family relationships through staying active, consequently improving physical activity behavior. Conclusion: These findings suggest that a resilient healthcare system in rural Nigeria may encourage a proactive stance toward positive behavior and increase healthcare utilization among older adults during crises. We recommend intensifying health literacy campaigns, including digital health literacy, eliminating the digital divide, and creating care protocols tailored for older adults to facilitate optimum healthcare access during crises in rural settings.
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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 | 0.003 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".