Multiprofessional Views on Older Patients’ Participation in Care Planning Meetings in a Hospital Context
Bibliographic record
Abstract
Care planning meetings (CPMs; sometimes referred to as family meetings) for older patients involve group decision-making between the multidisciplinary team, the older person and their family. However, service user participation is challenged by the inequity of knowledge and power between participants, together with organisational and resource pressures for timely discharge. The effective use and perhaps, potential misuse of communication strategies within CPMs is of ethical concern to all participants. Habermas’ essential critique of participatory communication provides insight as to how older people’s involvement can be either enabled or blocked by healthcare professionals (HCPs) depending on their use of communication strategies. Seven discipline-specific mini-focus groups provided an opportunity for HCPs to reflect on the participation of patients over 65 and their families in CPMs. Findings explore HCPs’ understanding of older patients involvement based on key dimensions of communicative participation, namely, mutuality, inclusiveness, patient centredness and clear outcomes. Whilst the benefits of collaborative decision-making were confirmed, legitimate concerns as to the quality of participatory practices, limited attention to group work processes and the exclusion of older patients with cognitive impairment were identified.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".