A critical analysis of health promotion and ‘empowerment’ in the context of palliative family care‐giving
Bibliographic record
Abstract
Traditionally viewed as in opposition to palliative care, newer ideas about 'health-promoting palliative care' increasingly infuse the practices and philosophies of healthcare professionals, often invoking ideals of empowerment and participation in care and decision-making. The general tendency is to assume that empowerment, participation, and self-care are universally beneficial for and welcomed by all individuals. But does this assumption hold for everyone, and do we fully understand the implications of health-promoting palliative care for family caregivers in particular? In this study, we draw on existing literature to highlight potential challenges arising from the application of 'family empowerment' strategies in palliative home-care nursing practice. In particular, there is a risk that empowerment may be operationalized as transferring technical and medical-care tasks to family caregivers at home. Yet, for some family caregivers, a sense of security and support, as well as trust in professionals, may be equally if not more important than empowerment. Relational and role concerns may also at times take precedence over a desire for empowerment. The potential implications of 'family empowerment' are explored in this regard. 'Family empowerment' approaches need to be accompanied by a strong understanding of how to best support individual palliative family caregivers.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".