A scoping research literature review to assess the state of existing evidence on the “bad” death
Bibliographic record
Abstract
OBJECTIVE: A scoping research literature review on "bad death" was undertaken to assess the overall state of the science on this topic and to determine what evidence exists on how often bad deaths occur, what contributes to or causes a bad death, and what the outcomes and consequences of bad deaths are. METHOD: A search for English-language research articles was conducted in late 2016, with 25 articles identified and all retained for examination, as is expected with scoping reviews. RESULTS: Only 3 of the 25 articles provided incidence information, specifying that 7.8 to 23% of deaths were bad and that bad deaths were more likely to occur in hospitals than in community-care settings. Many different factors were associated with bad deaths, with unrelieved pain being the most commonly identified. Half of the studies provided information on the possible consequences or outcomes of bad deaths, such as palliative care not being initiated, interpersonal and team conflict, and long-lasting negative community effects. SIGNIFICANCE OF RESULTS: This review identified a relatively small number of research articles that focused in whole or in part on bad deaths. Although the reasons why people consider a death to be bad may be highly individualized and yet also socioculturally based, unrelieved pain is a commonly held reason for bad deaths. Although bad and good deaths may have some opposing causative factors, this literature review revealed some salient bad death attributes, ones that could be avoided to prevent bad deaths from occurring. A routine assessment to allow planning so as to avoid bad deaths and enhance the probability of good deaths is suggested.
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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 | 0.064 | 0.212 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.066 | 0.043 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".