Exploring perceptions of online calculators for identifying community-dwelling older people at risk of dying: A qualitative study
Bibliographic record
Abstract
Objectives: This study aimed to assess the acceptability, value, and perceived barriers of using electronic risk calculators for predicting and communicating the risk of death in community-dwelling older adults. Methods: One focus group and eight interviews were conducted with 16 participants with experience caring for patients or family members at end of life. A prototype mortality risk tool was used to anchor discussions. Data were analysed using a qualitative content analysis approach. Results: Five themes emerged: acceptability, communication, barriers to use, broadening the circle of care, and tool limitations. Participants found the tool helpful for preparation, planning, and providing care, but disagreed on its community availability. Personalized risk estimates were valued for facilitating early goals of care conversations and normalizing discussions about death. However, concerns were raised about the tool's interpretation for individuals with different language, cultural, or educational backgrounds. Conclusions: While electronic risk calculators were found to be acceptable, balancing autonomy with varying preferences for receiving the information and potential need for support is crucial. Innovation: Providing patient-oriented life-expectancy estimates can enhance decisional capacity and facilitate shared decision-making between patients, their families, and healthcare professionals. Further research is needed to explore effective communication of personalized risk tools and additional benefits, harms, and barriers to 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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".