A Triple Jeopardy: Inadequate Knowledge about COVID-19 among Older Persons with Psychiatric Diagnosis attending a Geriatric Centre in Southwest Nigeria.
Bibliographic record
Abstract
BACKGROUND: Older persons with mental illnesses have been affected by COVID-19 because of reduced access to routine health care, the adverse social impacts of preventive strategies and inadequate knowledge of the COVID-19 pandemic. Adequate knowledge is crucial to ensuring adherence to the right preventive practices. OBJECTIVES: This study evaluates the knowledge gaps about COVID-19 and preventive practices among older persons with psychiatric diagnoses (PD) in comparison with older persons with non-psychiatric diagnosis (nPD). METHODS: A hospital-based comparative study was conducted among older persons attending the sycho-geriatric and Healthy Ageing clinics of the Geriatric Centre, University College Hospital, Ibadan, Nigeria. Data were gathered with a semi-structured interviewer-administered questionnaire, and SPSS version 23 was used to analyse the data. Level of significance was set at 5%. RESULTS: 390 respondents aged 60 and above were sampled in the two groups: 195 with PD and 195 with nPD. Their mean age was PD:72.2 (±7.4) years and nPD:71.0 (±8.0) years. Majority were aware of the ongoing pandemic (PD:95.9%; nPD:96.4%). The use of facemask (PD:89.7%; nPD:86.7%) was the commonest preventive practice. Male gender (OR: 2.09, CI ;1.14-3.86, p = 0.018) and education (OR: 5.10, CI; 1.15-22.67, p=0.032) were predictors of knowledge among PD and nPD respectively. CONCLUSION: Older persons with psychiatric diagnoses have more gaps in their knowledge of COVID-19. Inadequate knowledge about COVID-19 could further put them in jeopardy of contracting the virus with its associated morbidity and mortality, in addition to the risk that old age and mental illness contribute. Health education programs about COVID-19 targeting the older population with mental illnesses would be beneficial.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".