'No control whatsoever': end-of-life care on a medical teaching unit from the perspective of family members
Bibliographic record
Abstract
BACKGROUND: In our institution, about one third of annual deaths occur on the general medical teaching unit. (MTU) The average patient dies on the MTU from non-malignant disease after 4 weeks in hospital, and approximately 20% of available beds on the MTU at any time are occupied by patients who will not survive to discharge, but quality of end-of-life care on the MTU is not routinely assessed. AIM: To identify areas for improvement in delivering high quality end-of-life care on the medical teaching unit. DESIGN: Qualitative study using semi-structured interviews. METHODS: Six months after the death of the patient, next of kin were sent a letter inviting participation; 75 family members were screened and 50 invitations were mailed out. Interviews were conducted in the home. Eliciting narratives and direct questioning about important aspects of end-of-life care were used. RESULTS: Six next of kin agreed to participate. All patients were described as seriously and chronically ill. None died of cancer. Deaths were not described as 'good', and some comments suggested that death was unexpected and not inevitable. There were few concerns about pain control or unnecessary suffering. Proactive efforts to provide prognostic information or end-of-life care were not described. Survival, not palliation, was of central importance. Consistent with this priority, satisfaction with care provided in the intensive care unit was high. Follow-up after death was desired, especially if autopsy results were available. DISCUSSION: Earlier discussions about treatment failure and end-of-life care, and the need for palliation, appear to be central to improving the quality of end-of-life care for patients dying on our medical teaching unit. Our results are consistent with other studies in this area.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".