When Hope Makes Us Vulnerable: A Discussion of Patient–Healthcare Provider Interactions in the Context of Hope
Bibliographic record
Abstract
When hope is discussed in bioethics' literature, it is most often in the context of 'false hopes' and/or how to maintain hope while breaking bad news to patients. Little or no time is generally devoted to the description of hope that supports these analyses. In this paper, I present a detailed description of hope, one designed primarily for the healthcare context. Noting that hope is an emotional attitude, four key aspects are explored. In particular, the function of imagination in hope is discussed in depth. Through an examination of the relationship between hope and vulnerability, I demonstrate how adequately describing hope can broaden the normative inquiry into the role of hope in healthcare. Three ways in which persons with hope can be vulnerable are illustrated, and the challenge of how healthcare providers can attend in moral ways to the hopes of patients is identified.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.046 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.016 | 0.021 |
| 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".