Palliative care: A need for a family systems approach
Bibliographic record
Abstract
OBJECTIVE: When a family member is faced with a terminal illness, the impending death presents a crisis and a challenge to the entire family as a system. This article highlights the importance of caring for a family when one member has a life-threatening illness, and describes the applicability of Family Systems Theory and its major tenets to the palliative cancer population. METHODS: A MedLine and CINAHL search of Family Systems Theory related papers was conducted. RESULTS: Research studies that have been done fail to capture the view of the entire family system, often limiting the perspectives of the family to one single member. The concepts of holism, balance, boundaries, and hierarchal subsystems must be addressed in the care of any family, including those who have a family member who is dying. SIGNIFICANCE OF RESULTS: A Family Systems Theory framework can be useful in helping health care providers, and particularly nurses, deliver optimal care to palliative cancer patients and their families and standardize the way research is done by providing an appropriate framework with which to study the family. In addition, the adoption of Family Systems Theory as the standard framework from which to study families in palliative care will provide consistency for future studies that is presently lacking. Finally, nursing interventions to care for the family are suggested based on Family Systems Theory.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".