Ethical issues relating to the inclusion of relatives as clients in the post-stroke rehabilitation process as perceived by patients, relatives and health professionals
Bibliographic record
Abstract
OBJECTIVE: To document the ethical issues regarding the systematic inclusion of relatives as clients in the post-stroke rehabilitation process. METHODS: A two-phase qualitative design consisting of in-depth interviews with relatives and stroke-clients (Phase 1) and three focus groups with relatives, stroke-clients and health professionals (Phase 2). Data was audio recorded. Transcribed interviews and focus groups content were rigorously analyzed by two team members. RESULTS: The interview sample was composed of 25 relatives and of 16 individuals with a first stroke whereas the three focus group sample size varied from 5 to 7 participants. Four main themes emerged: (1) overemphasis of caregiving role with an unclear legitimacy of relative to also be a client; (2) communication as a key issue to foster respect and a family-centered approach; (3) availability and attitudes of health professionals as a facilitator or a barrier to a family-centered approach; and (4) constant presence of relatives as a protective factor or creating a perverse effect. CONCLUSION/PRACTICE IMPLICATIONS: The needs of relatives are well known. The next step is to legitimize their right to receive services and to acknowledge the combined clinical and ethical value of including them post-stroke. Interdisciplinary health care approaches and communication skills should be addressed.
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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 | 0.159 | 0.226 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".