Using a self-directed workbook to support advance care planning with long term care home residents
Bibliographic record
Abstract
BACKGROUND: While advance care planning (ACP) has been shown to improve the quality of end-of-life (EOL) communication and palliative care, it is rarely practiced in long term care (LTC) homes, where staff time to support the process is limited. This study examines the potential of a publicly available self-directed ACP workbook distributed to LTC residents to encourage ACP reflection and communication. METHODS: Recruitment took place across three LTC homes, between June 2018 and July 2019. To be eligible, residents had to have medical stability, cognitive capacity, and English literacy. The study employed a mixed methods concurrent design using the combination of ranked (quantitative) and open (qualitative) workbook responses to examine documented care preferences and ACP reflections and communications. RESULTS: 58 residents initially agreed to participate in the study of which 44 completed self-directed ACP workbooks. Our combined quantitative and qualitative results suggested that the workbooks supported the elicitation of a range of resident care preferences of relevance for EOL care planning and decision making. For example, ranked data highlighted that most residents want to remain involved in decisions pertaining to their care (70%), even though less than half expect their wishes to be applied without discretion (48%). Ranked data further revealed many residents value quality of life over quantity of life (55%) but a sizable minority are concerned they will not receive enough care at EOL (20%). Open comments affirmed and expanded on ranked data by capturing care preferences not explored in the ranked data such as preferences around spiritual care and post mortem planning. Analysis of all open comments also suggested that while the workbook elicited many reflections that could be readily communicated to family/friends or staff, evidence that conversations had occurred was less evident in recorded workbook responses. CONCLUSIONS: ACP workbooks may be useful for supporting the elicitation of resident care preferences and concerns in LTC. Developing follow up protocols wherein residents are supported in communicating their workbook responses to families/friends and staff may be a critical next step in improving ACP engagement in LTC. Such protocols would require staff training and an organizational culture that empowers staff at all levels to engage in follow up conversations with residents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".