A scoping review of decision‐making tools to support substitute decision‐makers for adults with impaired capacity
Bibliographic record
Abstract
BACKGROUND: Substitute decision-makers (SDMs) make decisions that honor medical, personal, and end-of-life wishes for older adults who have lost capacity, including those with dementia. However, SDMs often lack support, information, and problem-solving tools required to make decisions and can suffer with negative emotional, relationship, and financial impacts. The need for adaptable supports has been identified in prior meta-analyses. This scoping review identifies evidence-based decision-making resources/tools for SDMs, outlines domains of support, and determines resource/tool effectiveness and/or efficacy. METHODS: The scoping review used the search strategy: Population-SDMs for older adults who have lost decision-making capacity; Concept-supports, resources, tools, and interventions; Context-any context where a decision is made on behalf of an adult (>25 years). Databases included MEDLINE, Embase, CINAHL, PsycINFO, and Abstracts in Social Gerontology and SocIndex. Tools were scored by members on the research team, including patient partners, based on domains of need previously identified in prior meta-analyses. RESULTS: Two reviewers independently screened 5279 citations. Articles included studies that evaluated a resource/tool that helped a family/friend/caregiver SDMs outside of an ICU setting. 828 articles proceeded onto full-text screening, and 25 articles were included for data extraction. The seventeen tools identified focused on different time points/decisions in the dementia trajectory, and no single tool encompassed all the domains of caregiver decision-making needs. CONCLUSION: Existing tools may not comprehensively support caregiver needs. However, combining tools into a toolkit and considering their application relevant to the caregiver's journey may start to address the gap in current supports.
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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 | 0.025 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".