Death Anxiety among Older Adults during the COVID-19 Pandemic: Implications for Nursing Practice
Bibliographic record
Abstract
Death anxiety is a worldwide phenomenon among diverse populations, including older adults. However, few studies were located in a literature review that examined how the Covid-19 pandemic influenced the perception of death anxiety among the older adult population. Therefore, the purpose of this scoping review article is two-fold: (1) to provide an introductory discussion, based on the literature, regarding how the Covid-19 pandemic and its precautionary measurements provoke death anxiety, including its sub-category of predatory death anxiety, among older adults; and (2) to identify non-pharmacological interventions specific to death anxiety management for gerontological nurses to use during the Covid-19 pandemic or similar pandemics in the future. An intended outcome of this discussion paper is an enhanced understanding of ways to provide effective psychological care to older adults. The focus of discussion includes: the role of sociocultural factors, predatory death anxiety and Terror Management Theory, salient nursing assessment parameters and non-pharmacological interventions to address death anxiety among this population of older adults. In conclusion, gerontological nurses need to demonstrate evidence-based practice taking into consideration their own definition and perceptions of death, the reasons for their beliefs, and the cultural, situational, and spiritual context, in which they practice.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".