Fostering Existential Maturity to Manage Terror in a Pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic has created an environment in which existence is more fragile and existential fears or terror rises in people. Objective: Managing existential terror calls for being mature about mortality, something with which palliative care providers are familiar and in need of greater understanding. Methods: Using a case to illustrate, we describe existential terror, terror management, and existential maturity and go on to outline how existential maturity is important for not only the dying and the grieving but for also those facing risk of acquiring COVID-19. Results: Next, we describe how essential components in attaining existential maturity come together. (1) Because people experience absent attachment to important people as very similar to dying, attending to those experiences of relationship is essential. (2) That entails an internal working through of important relationships, knowing their incompleteness, until able to “hold them inside,” and invest in these and other connections. (3) And what allows that is making a meaningful connection with someone around the experience of absence or death. (4) We also describe the crucial nature of a holding environment in which all of these can wobble into place. Discussion: Finally, we consider how fostering existential maturity would help populations face up to the diverse challenges that the pandemic brings up for people everywhere.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".