Death denial: obstacle or instrument for palliative care? An analysis of clinical literature
Bibliographic record
Abstract
As a society and as individuals, we have come to recognize ourselves as 'death-denying', a self-characterisation particularly prominent in palliative care discourse and practice. As part of a larger project examining death attitudes in the palliative care setting, a Medline search (1971 to 2001) was performed combining the text words 'deny' and 'denial' with the subject headings 'terminal care', 'palliative care' and 'hospice care'. The 30 articles were analysed using a constant comparison technique and emerging themes regarding the meaning and usage of the words deny and denial were identified. This paper examines the theme of denial as an obstacle to palliative care. In the articles, denial was described as an impediment to open discussion of dying, dying at home, stopping 'futile' treatments, advance care planning and control of symptoms. I suggest that these components of care together constitute what has come to be perceived as a correct 'way to die'. Indeed, the very conceptualisation of denial as an obstacle to these components of care has been integral to building and sustaining the 'way to die' itself. The personal struggle with mortality has become an important instrument in the public problem of managing the dying process.
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 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 | 0.024 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.043 | 0.056 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".