Bibliographic record
Abstract
OBJECTIVE: The "good death" is a dynamic concept and has evolved over time to become a "revivalist" good death: a planned, peaceful, and dignified death, at home, surrounded by family members. As the "good death" continues to evolve, the key questions are: How do cultural perceptions of death and dying change? What are the forces that shape Western attitudes and beliefs around death and dying? And how does the "good death" discourse frame the dying experience in contemporary society? The purpose of this manuscript is to describe the underlying discourse in the literature on the "good death" in Western societies. METHOD: An integrative literature review of data from experimental and nonexperimental sources in PubMed, CINAHL, PsychINFO, and SocINDEX of 39 articles from 1992 to 2014. RESULTS: Four main themes emerged from reviewing 39 articles on the "good death": (1) the "good death" as control, (2) the wrong "good death," (3) the threatened "good death," and (4) the denial of dying. SIGNIFICANCE OF RESULTS: Evolving in response to prominent social attitudes and values, the contemporary "good death" is a powerful, constraining discourse that limits spontaneity and encourages one way to die. Social, political, and demographic changes now threaten the stability of the "good death"; dying is framed as an increasingly negative or even unnecessary process, thus marginalizing the positive aspects of dying and rendering dying absent, invisible.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".