Importance of Family-Centred Care to Palliative Medicine
Bibliographic record
Abstract
The family is inevitably involved in care-provision when one of its number suffers from a progressive and life-threatening illness such as advanced cancer. Distress reverberates throughout the family, with moderate rates of psycho-social morbidity, including up to one third ofpartners and one quarter of adult children (1-3). There has been growing awareness over recent years of the importance of a family-centred model of care to fully meet the needs of patients and families involved with palliative care services and, moreover, maintain continuity of support into bereavement (4). To achieve this, we need both conceptual and pragmatic methods of classifying families to guide our efforts at interven-tion. Historically, one approach has been to conceptualize families in terms of the phase of illness they must negotiate (5); another stressed the family's needs or the associated burdens it experienced (6); a third focused on the family's developmental stage (7). Yet none of these approaches proved predictive of psycho-social outcome over time. It was not until attention turned to family functioning that a clinically useful method of predicting psychosocial outcome emerged (8,9). Through these longitudinal studies of families during palliative care and bereavement, we were able to classify families using the following dimensions of their functioning: (i) cohesiveness, the family's sense of togetherness; (ii) expressiveness, their sharing of both thoughts and feelings; and (iii) conflict resolution. These dimensions form a simple screening instrument, the 12 item
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".