Qualitative Analysis of Coping Strategies Among Older Persons During COVID-19 Lockdown in Nigeria: Considerations for Community Health Promotion
Bibliographic record
Abstract
Background: The challenge of COVID-19 and the disease control restrictions in Nigeria directly affected social life. Older adults were disproportionately affected because of social vulnerabilities, socio-economic dependency, and the high risk of COVID-19 infectious disease in the population. Purpose: To this end, the study explored the instrumental coping strategies of older adults duirng the COVID-19 lockdown in Nigeria. Research Design: An exploratory research design using qualitative approach based on in-depth interviews was adopted. Study Sample: Twenty-seven (27) older adults aged 60 years and more in Ibadan, Nigeria were purposively selected for participation in the research. Data Collection and/or Analysis: Community living older adults participated in in-depth telephone interviews during the COVID-19 restrictions. Using Atlas ti 8.4, the 27 transcripts were analyzed and validated through content analysis. Results: Older adults coped with fear, anxiety, depression, and other health challenges, and three major themes emerged as problem-focused coping strategies. Older adults explored spirituality, engaged in their social networks through telephones, and focused on a healthy diet and physical activities. Conclusions: Community health promotion strategies for older adults should explore the identified problem-focused coping strategies to optimize the health and wellbeing of older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".