Exploring the Acceptability and Relevance of Tool-supported Advance Care Planning (ACP) for a Long-term Care (LTC) Home Environment
Bibliographic record
Abstract
Abstract Objectives: Despite known benefits, advance care planning (ACP) is rarely a component of usual practice in long-term care (LTC). A series of tools and workbooks have been developed to support ACP uptake. Yet, their acceptability and relevance for LTC has yet to be examined. This study explored the extent to which available ACP materials hold promise in improving ACP engagement in LTC by (1) exploring LTC home staff’s reactions to tool supported ACP and (2) examining if available ACP tools include content of relevance to LTC. Methods: A combination of focus group deliberations with LTC home staff (N=32) and content analysis of publicly available ACP workbooks (N=32) were used to meet the study aims. Results: Focus group deliberations suggested that tool-directed ACP is a promising approach for LTC, provided tools include psychosocial elements and are paper-based. Content analysis of available tools revealed that only a handful of paper-based ACP tools (32/611, 5%) include, psychosocial content, with most encouraging psychosocially-oriented reflections (30/32, 84%), and far fewer providing direction around other elements of ACP such as communicating psychosocial preferences (14/32, 44%) or transforming preferences into a documented plan (7/32, 22%).Conclusions: Tool-supported ACP appears acceptable to LTC staff. To improve ACP uptake in LTC selected tools should include psychosocial content that can be supported by a range of clinical and non-clinical staff. Available ACP tools may require infusion of scenarios pertinent to frail older persons, and a better balance between psychosocial content that elicits reflections and psychosocial content that supports communication.
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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 | 0.034 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".