Exploring quality of life among the elderly in Hai Duong province, Vietnam: a rural–urban dialogue
Bibliographic record
Abstract
BACKGROUND: Quality of life (QoL) is an important health index for the elderly, necessary for assessing interventions, and prioritising medical and social care needs. As the ageing population in Vietnam continues to increase, understanding important dimensions of QoL for the elderly is essential. There is a paucity of research in this area, however, and the available literature focuses on functional capacities. The purpose of this article is to explore perceptions on the dimensions of QoL among the elderly in Vietnam, to use these perceptions to broaden the concept, and to explore similarities and differences between those living in urban compared to rural areas. METHOD: Qualitative methods included in-depth interviews (IDI) with experts in ageing and elderly persons, as well as focus group discussions (FGDs) in three communes in Hai Duong province. IDIs and FGDs were recorded and transcribed. NVivo software was used to analyse the data. RESULTS: Thematic analysis identified physical, psychological, social, environmental, religious, and economic as important dimensions of QoL. For elderly participants in both urban and rural areas, physical health, social relations, finances and economics, the physical and social environment, and psychological health were reported as important. Rural participants also identified religious practice as an important dimension of QoL. In terms of relationships, the elderly in urban areas prioritised those with their children, while the elderly in rural areas focussed their concerns on community relationships and economic conditions. CONCLUSION: Isolating individual factors that contribute to QoL among the elderly is difficult given the inter-relations and rich cross-linkages between themes. Elderly participants in urban and rural areas broadly shared perspectives on the themes identified, in particular social relationships, but their experiences diverged around issues surrounding finances and economics, their respective physical and social environments, and the contribution of religious practice. The study findings may help provide guidance for the development of a socially and culturally relevant instrument for measuring QoL among the elderly in Vietnam. The results will also be useful for developing policies and interventions that are responsive to the needs of the elderly, and reflect the themes perceived to be important.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".