Morbidity pattern and factors that influence family dynamics among elderly attending selected Primary Care Centres in Lagos
Bibliographic record
Abstract
Background: The most vulnerable and high-risk groups in terms of morbidity and health status is the elderly. With ageing comes deterioration of functional capacity of an individual. The elderly usually has co-morbidities which results in a rapid decline in their health status. Family dynamics constitute the pattern of interactions between the family members. This is influenced by health status of the members. The health of the elderly is affected by social factors which involves children leaving their parents in search for jobs, leaving their parents isolated without support in daily activities and health-care system factors such as lack of effective health insurance system for elderly.Objective: To determine the morbidity pattern and family dynamics among elderly in selected Primary Healthcare Centres in LagosMethods: A cross sectional study was carried out on 207 elderly patients. A multistage simple random sampling technique was used to select respondents and data was collected by using questionnaires from past studies to collect information on morbidity pattern, family type, family model, healthcare financing and satisfaction. The obtained data was analyzed with SPSS version 20 software.Results: Of all the respondents, 133 (64.3%) were married, 64 (30.9%) widowed while 4.8% were either divorced or separated. Majority of the respondents, 113(54.6%) practiced monogamy. More than three quarter of the respondents live with family/caregivers. More than half of the respondents (53.1%) were unemployed, 74 (35.8%) self-employed while the remaining 23 (11.1%) were pensioners. About 94% paid out of pocket for their services, only 12 (5.8) were enrolled on NHIS. There were significant associations between Ischemic heart disease, tremor and satisfaction with service quality. Polygamy was found to be associated with Back pain, Parkinson disease, Depression, Arthritis, Hearing impairment. Interacting type of family model was found to be associated with Asthma, Hypertension, and Ischemic Heart Disease.Conclusion: The most prevalent medical condition in the elderly was Hypertension followed by back pain. Out of pocket was the most common (94.2%) means of payment for medical bills. Based on Stevenson's family model, the Interacting type was constituted by 87.9% of the respondents while the remaining proportion was made up of actualizing type.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".