Geriatric assessment in older adults with cancer: A global scoping review of barriers and strategies to implementation
Bibliographic record
Abstract
INTRODUCTION: There is a notable gap in practical application of evidence synthesis from systematic exploration and summarization of barriers and strategies for implementing geriatric assessment (GA) in older adults with cancer globally. This review systematically mapped out and synthesized the evidence on barriers and strategies for GA implementation in older adults with cancer. MATERIALS AND METHODS: A comprehensive, systematic search across seven electronic databases (MEDLINE, PsycINFO, CINAHL, Web of Science, Proquest, Scopus, and Ageline) was performed to identify peer-reviewed articles from January 1, 2013 to October 30, 2024. The scoping review followed the JBI methodology for scoping reviews guidelines and adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension. The protocol was registered on Open Science Framework. Two researchers independently screened 2871 records and extracted relevant data from 34 full-text study reports and performed content analyses to consolidate the findings. RESULTS: The 34 articles were classified into the following categories: cross-sectional surveys (n = 16), observational cohort studies (n = 7), randomized controlled trials (n = 3), single-arm intervention trials (n = 4), qualitative studies (n = 2), and mixed methods (online surveys and interviews) (n = 2). The articles included were from 12 countries, with one global collaboration, and the majority (29/34) were published in the past five years. Five themes describing 27 barriers influencing the implementation of GA in older adults with cancer were summarized. Seventeen implementation strategies were identified and summarized from content analyses of the included articles. An initial draft of the barriers and strategies guide template for geriatric assessment (BeST-GA) was developed as a synthesized summary of the findings from this scoping review. DISCUSSION: The literature confirms that barriers affecting GA implementation in older adults with cancer and the strategies to overcome them are unique to individual settings and require a tailored approach. The draft BeST-GA guide template should be further tested and may be used as a first step to assess the setting during the planning phase of GA implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".