Older adults’ experiences of health seeking in rural areas in low- and middle-income countries: a systematic review of qualitative studies
Bibliographic record
Abstract
The global aged population is expected to reach 2.1 billion by 2050 and ∼40% of them will live in rural areas of low- and middle-income countries (LMICs). This systematic review aims to synthesize the qualitative literature on rural older adults' experiences of health-seeking in LMICs as well as explore the factors that influence their experiences during their health-seeking journeys. We searched Embase, MEDLINE, PsycINFO, and CINAHL to identify studies published from 1 January 2002 to 31 December 2024 (PROSPERO registration ID: Blinded For Review). We used a thematic synthesis approach to analyse included studies. Among the 19 studies with 28 articles and 484 participants included, 16 were rated as high quality, 9 as moderate quality, and 3 as weak quality. We identified four primary analytic domains associated with their experiences in health-seeking journeys: (i) individual-depicting the inner world of rural older adults; (ii) interpersonal-navigating the rural social network; (iii) organizational-navigating the rural health care systems, and; (iv) community and macrosystems-economy, society, and public policy in rural areas. Rural older adults in LMICs have experienced unique and multi-level challenges in seeking care. To overcome these challenges, rural older adults demonstrated resilience and creativity (e.g. utilizing informal institutions), to navigate their health-seeking journey. Future research should aim to better understand the resilience and agency in local older adults' health-seeking experiences and provide constructive solutions to overcome identified barriers to care.
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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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".