COVID-19: Experiences of Social Workers Supporting Older Adults With Dementia in Nigeria
Bibliographic record
Abstract
Amidst the COVID-19 pandemic, numerous public health protocols were instituted by government agencies to safeguard individuals with dementia, their family caregivers, and formal care providers. While these preventive measures were implemented with good intentions, they inadvertently imposed significant challenges on medical social workers in Nigeria. This paper explored the experiences of medical social workers caring for people with dementia during the COVID-19 pandemic in Nigeria. Twenty-six medical social workers from 6 government hospitals in Southwestern Nigeria participated in an in-depth interview. The research reveals 3 pivotal aspects: Firstly, the escalating demands within the work environment, where medical social workers grapple with the intricate task of conveying sensitive information about dementia diagnosis and COVID-19 prevention protocol, managing expectations regarding dementia diagnoses, and navigating resource constraints for individuals with dementia during the pandemic. Secondly, discernible impacts on the work climate and interprofessional relationships shed light on the challenges these professionals face in collaborating with other healthcare providers. Lastly, the reverberations on social workers' personal lives underscore the pandemic's toll on their well-being. Thus, the findings underscore the need for proactive measures to equip medical social workers to face the distinctive challenges in dementia care during future pandemics. Recognizing the potential resurgence of global health crises, the research highlights the need for strategic preparedness to mitigate the impact of future pandemics on the well-being of individuals with dementia and the professionals dedicated to their care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".