“Now I Don’t Have to Guess”: Using Pamphlets to Encourage Residents and Families/Friends to Engage in Advance Care Planning in Long-Term Care
Bibliographic record
Abstract
Objective: This article explores whether access to illness trajectory pamphlets for five conditions with high prevalence in long-term care (LTC) can encourage residents and families/friends to openly engage in advance care planning (ACP) discussions with one another and with health providers. Method: In all, 57 residents and families/friends in LTC completed surveys and 56 participated in seven focus groups that explored whether the pamphlets supported ACP engagement. Results: Survey results suggested that access to pamphlets encouraged residents and families/friends to reflect on future care (48/57, 84%), clarified what questions to ask (40/57, 70%), and increased comfort in talking about end of life (EOL) care (36/57, 63%). Discussions between relatives and friends/families (32/57, 56%) or with health providers (21/57, 37%) were less common. Focus group deliberations illuminated that while reading illness-specific information was validating, a tendency to protect one another from an emotional topic, prevented residents and families/friends from conversing with one another about EOL issues. Discussion: Having access to pamphlets with information about EOL care provides important and welcome opportunities for reflection for both residents in LTC and their families/friends. Moving residents and families/friends from reflecting on issues to discussing them together could require staff support through planned care conferences or staff initiated conversations at the bedside.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".