A systematic review and qualitative analysis of geriatric models of care for rural and remote populations
Bibliographic record
Abstract
INTRODUCTION: Much is known about the healthcare needs of rural and remote communities; however, understanding how to best deliver geriatric models of care in these settings has received less attention. The purpose of this systematic review was to identify necessary key components of existing models of geriatric care serving rural or remote populations. METHODS: A systematic literature review was conducted using MEDLINE, CINAHL and EMBASE databases to identify articles that described models of geriatric care serving rural or remote populations. A qualitative case study and key component analysis approach was used to identify necessary model components. RESULTS: Eight articles were included. We identified eight distinct components that may improve the successful delivery of models of geriatric care serving rural or remote populations. Environmental assessments were done in six of eight models. Model integration with the local healthcare system, local provider leadership, and local provider education in geriatrics were present in five of eight models. Three of eight models used high-risk screening principles and included geriatrician consultation. One model described active community engagement, and one used telemedicine. CONCLUSION: Future geriatric care delivery models designed to serve rural or remote populations are encouraged to use an evidence-based framework based on eight distinct model characteristics found in the literature that aim to support the ideal provision of effective and accessible geriatric medical 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 | 0.033 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".