Exploring the priorities of ageing populations in Pakistan, comparing views of older people in Karachi City and Thatta
Bibliographic record
Abstract
As a lower middle-income country, Pakistan faces multiple issues that influence the course of healthy ageing. Although there is some understanding of these issues and the objective health outcomes of people in Pakistan, there is less knowledge on the perceptions, experiences, and priorities of the ageing population and their caretakers (hereafter, "stakeholders"). The aim of the study was to identify the needs and priorities of older adults and stakeholders across both urban and rural locations. We sought to explore the views of two groups of people, older adults and stakeholders on topics including the definition of ageing as well as areas of importance, services available, and barriers to older people living well. Two-day workshops were conducted in one rural city, Thatta and one urban city, Karachi. The workshops were designed using the Nominal Group Technique, which included plenary and roundtable discussions. The responses were ranked through rounds of voting and a consensus priority list was obtained for each topic and group. Responses were categorized using the socio-ecological framework. Responses were compared between stakeholders and older people and between different geographical areas. 24 urban and 26 rural individuals aged over 60 years and 24 urban and 26 rural stakeholders attended the workshops. There were few areas of agreement with respect to both geographical region and participant group. Comparing older adults' definition of ageing, there was no overlap between the top five ranked responses across urban and rural locations. With respect to areas of importance, there was agreement on free health care as well as financial support. In terms of barriers to healthy ageing, only nation-wide inflation was ranked highly by both groups. In addition, there were relatively few areas of congruence between stakeholder and older adult responses, irrespective of location, although engagement with family, adequate nutrition and monetary benefits were responses ranked by both groups as important for healthy ageing. Both groups ranked issues with the pension system and financial difficulties as barriers. When categorized using the socio-ecological model, across all questions, societal factors were prioritized most frequently (32 responses), followed by individual (27), relationship (15), and environment (14). Overcoming barriers to facilitate healthy ageing will require a multi-faceted approach and must incorporate the priorities of older individuals. Our results may serve as a guide for researchers and policymakers for future engagement and to plan interventions for improving the health of the ageing population in Pakistan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".