Voices lost: where is the person in evaluating a palliative approach to care?
Bibliographic record
Abstract
Person-centredness is a cornerstone to a palliative approach to care. However, there is a risk that a person-centred perspective is lost in how a palliative approach is evaluated. We explored the extent to which evaluations of a palliative approach are consistent with its person-centred ethical stance. Using a narrative review approach, we critically reflected on how the experiences, priorities and concerns of patients and family are represented, or not represented, in evaluations of a palliative approach. We were guided by the following questions: (1) What types of outcomes and indicators are commonly used to evaluate a palliative approach? (2) Whose perspectives are represented in current evaluations of a palliative approach? And (3) What are the foci of evaluation in this body of research? We observed that the evaluations of a palliative approach are commonly based on indicators of its implementation and predominantly reflect the perspectives of healthcare providers and healthcare systems, rather than patients or family. Although evaluations focused on healthcare providers and systems are important for integrating a palliative approach, there is concern that the essence of person-centredness is lost when the perspectives of patients and families about their healthcare needs, outcomes and experiences are not consistently measured as the ultimate goal of care. There is a need for more emphasis on evaluation practices that value person-centred outcomes, in addition to outcomes oriented to the needs of healthcare providers and systems.
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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 | 0.127 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.018 | 0.042 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".