Exploration of the acceptability and usability of advance care planning tools in long term care homes
Bibliographic record
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 amongst the generable population. Yet, their potential for improving ACP uptake in LTC has yet to be examined. This study explored if available ACP tools are acceptable for use in LTC by (a) eliciting staff views on the content and format that would support ACP tool usability in LTC (b) examining if publicly available ACP tools include content identified as relevant by LTC home staff. Ultimately this study aimed to identify the potential for existing ACP tools to improve ACP engagement in LTC. METHODS: A combination of focus group deliberations with LTC home staff (N = 32) and content analysis of publicly available ACP tools (N = 32) were used to meet the study aims. RESULTS: Focus group deliberations suggested that publicly available ACP tools may be acceptable for use in LTC if the tools include psychosocial elements and paper-based versions exist. Content analysis of available paper-based tools revealed that only a handful of 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: ACP tools that include psychosocial content may improve ACP uptake in LTC because they elicit future care issues considered pertinent and can be supported by a range of clinical and non-clinical staff. To increase usability and engagement 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 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".