The Hidden Nature of Death and Grief
Bibliographic record
Abstract
Western culture can be described as death-denying and youth-obsessed. Yet this has not always been the case. Only a few generations ago, death was very much part of life where people died at home with their families members caring for them. A shift occurred, in part, because of the unprecedented advances in medical science that the western world has seen over the past 40 years. Health care professionals now have the knowledge and the technology to prolong life in ways that were previously not only unattainable, but inconceivable. Regardless, the reality that death will eventually come for each of us has not changed; merely our perception of it has. This perception is influenced by the hidden nature of death in our society. This begs the questions: if death in our culture is something to hide, to conceal, and to keep secret, then what does that say about our ability to express grief? What does this mean for those who face it as part of their chosen profession? How might we understand the nature of suffering for those who turn toward the suffering of others? This paper interpretively examines the nature of hidden death and hidden grief in our society.Keywords: death, grief, hermeneutics, hidden, pediatric care nursing
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".