Family Caregivers’ Decision-Support Needs Beyond the Decision Aid
Bibliographic record
Abstract
Abstract Of the estimated 16 million U.S. family members currently providing essential yet unpaid caregiving for persons with dementia (PWD), many will also make end-of-life (EOL) care decisions as surrogates, a process that can be fraught with uncertainty. Even with dementia death rates rising, many families delay advanced care planning (ACP) discussions, and surrogate decision makers often lack crucial information and support, implicating the need to further study this topic in aging. While decision aids (DA) serve as a support tool for caregivers, they can be less effective when failing to address unresolved decisional needs. Utilizing the Ottawa Decision Support Framework (ODSF), which asserts caregiver decision needs affect decision quality, this study sought to identify surrogate decision-support needs extending beyond general ACP. This mixed study used cognitive interviews and focus groups with family caregivers (N=13) and healthcare professionals (n=14) to assess their knowledge and understanding of hospice and artificial hydration and nutrition. Data were audio-recorded, transcribed verbatim, and analyzed with thematic content analysis. Three main themes were identified: DAs alone aren’t enough to foster quality decision making for surrogates; individualized communication is necessary to clarify PWD and caregiver value priorities and disease trajectories; and clarification of the impact of care choices within situational contexts is quintessential. Further development is needed to create a practice protocol from these themes to inform professionals assisting surrogates in ACP at EOL. Practical implications from this study include highlighting the importance of individualized communication between PWD, providers, and caregivers in addressing EOL care decisional needs.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".