Psychosocial wellbeing and risk perception of older adults during COVID-19 pandemic in Nigeria: perspectives on the role of social workers
Bibliographic record
Abstract
Background: The COVID-19 pandemic presented a 'double-edged sword' for older adults: not only were they more susceptible to the virus, but its broader consequences also exacerbated other challenges, particularly those related to psychosocial well-being. Limited evidence exists on how older adults perceive the pandemic and its impact on their well-being and the role of social workers in addressing these challenges, particularly in resource-limited settings like Nigeria. Aim: This study explored older adults' perceived risks regarding COVID-19, its impact on their psychosocial well-being, and the role of social workers in addressing these challenges in Nigeria. Methods: A phenomenological and exploratory research design was used. In-depth interviews (IDIs) were conducted with 16 older adults and 4 social workers in Onitsha metropolis, Anambra State, Southeast Nigeria. Data were analyzed through reflexive thematic analysis. Results: The findings revealed that the COVID-19 restrictive measures negatively impacted the psychosocial well-being of older adults, where social isolation, lack of support, the inability to engage in wellbeing activities, and emotional trauma collectively contributed to a significant decline in their mental and emotional health. Additionally, widespread misconceptions about the origin of COVID-19 led to reluctance in adopting preventive measures. While social workers provided some awareness and counselling sessions, their involvement was limited. Social workers were not recognized as part of the frontline response team, and their efforts were primarily constrained by governmental and institutional neglect. Conclusion: The findings highlight the need for policy initiatives to enhance social workers involvement in strengthening the psychosocial resilience of older adults and addressing misconceptions during public health emergencies. Comprehensive strategies are essential for safeguarding the psychosocial well-being of older adults in future pandemics or similar crises.
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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 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".