A global scoping review of barriers and strategies to implementation of geriatric assessment in older adults with cancer.
Bibliographic record
Abstract
e13851 Background: There is a lack of practical application of evidence synthesized from the systematic exploration and summarization of barriers and strategies for implementing Geriatric Assessment (GA) in older adults with cancer on a global scale. This systematic scoping review aimed to map out and synthesize the evidence on barriers and strategies for GA implementation in older adults with cancer. Methods: A comprehensive, systematic search ofseven electronic databases (MEDLINE, PsycINFO, CINAHL, Web of Science, Proquest, Scopus, and Ageline) was conducted to identify peer-reviewed articles, from 1 st January 2013 to 30 th October 2024. Two researchers independently screened 2,871 records, extracted relevant data from 34 full-text study reports and conducted content analyses to synthesize 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 method (online surveys and interviews) (n = 2). The included articles were from 12 countries (United States (n = 7), The Netherlands (n = 5), Canada (n = 3), Japan (n = 4), France (n = 2), Australia (n = 3), United Kingdom (n = 2), Belgium (n = 1), Brazil (n = 1), India (n = 1), Mexico (n = 1), Portugal (n = 1)), with one global collaboration. Most study reports (29/34) were published in the past five years. Five overarching themes encompassing 27 barriers to implementing GA in older adults with cancer were identified and summarized. Additionally, 17 implementation strategies were extracted and synthesized through content analysis of the included articles. These findings informed the development of the initial draft of the Barriers and Strategies Checklist Template for Geriatric Assessment (BeST-GA), providing a comprehensive, consolidated summary of the scoping review's results. Conclusions: 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 checklist should be further tested and may be used as a first step to assess the setting during the planning phase of GA implementation.
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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 | 0.068 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".