Exploring implementation considerations for<scp>geriatric‐HIV</scp>clinics: A secondary analysis from a scoping review on<scp>HIV</scp>models of geriatric care
Bibliographic record
Abstract
OBJECTIVES: This review aimed to map the current state of knowledge regarding the implementation considerations of existing geriatric-HIV models of care, to identify areas of further research and to inform the implementation of future geriatric-HIV interventions that support older adults living with HIV. METHODS: We conducted a scoping review that was methodologically informed by the Arskey and O'Malley's 5 step framework and theoretically informed by the Consolidated Framework for Implementation Research (CFIR). A systematic search of six databases was conducted for peer-reviewed literature. The grey literature was also searched. Article screening was performed in duplicate. Data was extracted for the purpose of this secondary analysis using a data extraction template informed by the CFIR. Data was inductively and deductively analyzed. RESULTS: In total, 11 articles met the inclusion criteria. The models of care described varied in terms of their location and setting, the number and type of care providers involved, the mechanism of patient referral, the type of assessments and interventions performed and the methods of longitudinal patient follow-up. Four key categories emerged to describe factors that influenced their implementation: care provider buy-in, patient engagement, mechanisms of communication and collaboration, and available resources. CONCLUSIONS: The findings from this scoping review provide an initial understanding of the key factors to consider when implementing geriatric-HIV models of care. We recommend health system planners consider mechanisms of communication and collaboration, opportunities for care provider buy-in, patient engagement and available resources. Future research should explore implementation in more diverse settings to understand the nuances that influence implementation and care delivery.
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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 | 0.154 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".