Rural and urban differences in quality of dementia care of persons with dementia and caregivers across all domains: a systematic review
Bibliographic record
Abstract
BACKGROUND: There are challenges in healthcare service delivery in rural areas, and this may be especially true for persons with dementia, who have higher needs to access to the healthcare system, and may have difficulties to commute easily and safely to these services. There is a growing body of literature regarding geographical disparities, but there is no comprehensive systematic review of geographical differences in persons with dementia across all domains of care quality. Therefore, the objective of this study is to conduct a systematic review of the literature on rural and urban differences in quality of dementia care outcomes of persons with dementia across all quality-of-care domains. METHODS: We performed a digital search in Ovid MEDLINE on July 16, 2019, updated on May 3, 2021, for French or English records. We selected studies that reported outcome from at least one domain of quality of dementia care (Access, Integration, Effective Care, Efficient Care, Population Health, Safety, and Patient-Centered) in both rural and urban persons with dementia or caregivers. We used rigorous, systematic methods for screening, selection, data extraction and we analyzed outcomes reported by at least two studies using vote counting and appraised the certainty of evidence. Finally, we explored sources of heterogeneity. RESULTS: From the 38 included studies, we found differences in many dementia care domains. Rural persons with dementia had higher mortality rates (Population Health), lower visits to any physicians (Access), more hospitalizations but shorter stays (Integration), higher antipsychotic medications (Safety), lower use of home care services and higher use of nursing home (Patient-Centered Care) compared to urban persons with dementia. CONCLUSIONS: This comprehensive portrait of rural-urban differences in dementia care highlights possible geographically based inequities and can be used by researchers and decision makers to guide development of more equitable dementia care policies.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".