Perceived family support and undernutrition among older outpatients of a Northern Nigerian hospital: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: Population ageing is increasing in developing countries like Nigeria, where declining death rates and high birth rates raise public health concerns, particularly regarding undernutrition. The shift toward nuclear family structures has diminished the support older adults receive from family members, compounded by economic challenges. This study seeks to explore the relationship between perceived family support and undernutrition among older adults, aiming to provide insights for enhancing health outcomes through improved family networks. METHODS: The sequential mixed-methods study (cross-sectional study of 145 older adults followed by in-depth interviews of the identified undernourished older adults) was conducted in the Geriatric unit of a General Outpatient Clinic in Kano. Inferential statistical analyses were used to determine the associations between family support and undernutrition. Thematic analysis of the text data from the interviews was done using Nvivo® version 12 pro. RESULTS: The mean age of respondents was 69.08 ± 7.82 (60-95) years; 76 (52.4%) were females. The prevalence of undernutrition was 15.2% and poor family support was 36.6%. Older age ≥ 75 (aOR=22.59, 95%CI = 5.45-93.57, P < 0.001), and poor family support (aOR=9.31, 95%CI = 2.32-37.42, P = 0.002) were the determinants of undernutrition in this study. Most undernourished older patients reported poor family interaction and satisfaction as the likely reasons for their condition. CONCLUSION: This study reported a high prevalence of undernutrition, with older age and poor family support as significant determinants. Undernutrition, driven by poor family support, financial hardships and limited food variety, emerged as recurring themes in the qualitative arm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".