Understanding proxy decision‐makers’ perspectives of frailty and frailty tools to support decision‐making for persons with dementia
Bibliographic record
Abstract
Abstract Background Proxy decision‐makers’ for persons with dementia (PwD) have identified feeling uninformed about the prognosis and progression of dementia and unsupported by healthcare professionals. One aim of our study was to explore how frailty is understood and if frailty tools may assist with decision‐making. Method We conducted a qualitative inquiry using focus groups to explore decision‐makers understanding of frailty and their perspectives of using frailty tools. Proxy decision‐makers were identified as formal agents of a PwD. Focus groups were recorded and transcribed verbatim. Data was analyzed and interpreted using thematic analysis. Result Analysis revealed the prevailing theme to be, frailty is understood as a mosaic. Decision‐makers’ primarily consider frailty a physical phenomenon, closely paralleling Fried’s (2000) frailty phenotype: weight loss, low activity, slow gait, weakness and fatigue. Participants also associated frailty with cognitive decline and emotional instability. Applying the term frailty to PwD produced an ambivalent response in participants, although the term itself was considered as acceptable to describe physical decline. Participants valued the Clinical Frailty Scale to aid in the understanding of the progression of physical decline, but felt the limited recognition of cognitive decline was a barrier. The fluctuating course of dementia also limited the utility of using a tool with fixed categories. Conclusion Frailty is a recognizable term to proxy decision‐makers, but is understood in a variety of ways, primarily as a physical phenomenon. Given the ambivalence in using this term and decision‐makers’ focus on cognitive changes, discussing frailty has little to add to improve supporting decision‐making in dementia.
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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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".