Development of communication tool for resident‐ and family‐led care discussions in long‐term care through patient and family engagement
Bibliographic record
Abstract
BACKGROUND: Effective communication between residents (older adults), families, and the healthcare team supports person-centred care. However, communication breakdowns can occur that can impact care and outcomes. The aim of this paper is to describe a feedback approach to developing a communication tool for residents and families to guide information sharing during care discussions with the healthcare team in long-term care. METHODS: Development of the communication tool included consultation with key stakeholders for their feedback and input. Following initial development of the tool template by our research team, we invited feedback from our study collaborators. Next, individual interviews and a focus group were conducted with family members, followed by individual interviews with selected residents from two long-term care homes in Ontario, Canada. Participants were asked to provide input and feedback on the tool's content and usability and to share ideas for improving the tool. Content analysis was used to analyse the interview data. RESULTS: Feedback from residents and family included suggestions to enhance the tool's content and use of plain language, and suggestions for potential application of the tool. CONCLUSION: Feedback highlighted the value of engaging residents and family members in the development of a communication tool. The communication tool offers a structured format to support participation of residents and families in information sharing for care discussions with the healthcare team.
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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 | 0.038 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".