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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".