Involvement of supportive care professionals in patient care in the last month of life
Bibliographic record
Abstract
BACKGROUND: In the last month of life, many patients suffer from multiple symptoms and problems. Professional supportive care involvement may help to alleviate patients' suffering and provide them with an optimal last phase of life. PURPOSE: We investigated how often palliative care consultants, pain specialists, psychological experts and spiritual caregivers are involved in caring for patients in the last month of life, and which factors are associated with their involvement. METHODS: Questionnaires were mailed to physicians who had attended the death of a patient from a stratified sample of 8496 deaths that had occurred in 2010 in the Netherlands. The response rate was 74 % (n = 6263). RESULTS: A palliative care team or consultant had been involved in the last month of life in 12 % of all patients for whom death was expected; this percentage was 3 % for pain specialists, 6 % for psychologists or psychiatrists and 13 % for spiritual caregivers. Involvement of palliative care or pain specialists was most common in younger patients, in patients with cancer and in patients who died at home. Involvement of psychological or spiritual caregivers was most common in older patients, in females, in patients with dementia and in patients who died in a nursing home. Involvement of supportive caregivers was also associated with the use of morphine and end-of-life decisions. CONCLUSION: Supportive care professionals are involved in end-of-life care in about a quarter of all non-suddenly dying patients. Their involvement is related to the setting where patients die, to the patient's characteristics and to complex ethical decision-making.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".