The Possibility of Health Care Directives for Vaccinations—A Consultation Study With Relevant Stakeholders
Bibliographic record
Abstract
ABSTRACT Background and Aims Health care directives allow individuals to express their wishes about future health care treatments if they become unable to communicate their wishes (e.g., due to cognitive impairment). Because vaccinations have become contentious preventative treatments whereby future wishes might not be honored by others, vaccinations should be given specific consideration for potential inclusion in health care directives. The purpose of this project was to explore and receive preliminary feedback on: the idea of vaccination‐specific directives; support for draft statements; and practical implications. Methods We conducted two discrete online surveys with individuals in professions engaging with health care directives (e.g., health care, long‐term care, and legal), as well as advocates for older adults, in 2023 and 2024. In the first survey ( n = 39), we canvassed initial reactions to the idea of vaccination‐specific directives and requested feedback on draft statements. Using an iterative approach, we analyzed the first survey to craft the second survey ( n = 151), to receive feedback on a revised directive statement, and to identify dominant themes. Results Overall, there was broad support for the idea of vaccination‐specific directives. In the first survey, 87.2% agreed that it made sense to them. In the second survey, 83.3% of respondents agreed that the proposed statement was clear, with 72.0% indicating “yes” that the statement would result in a person receiving vaccines. Dominant themes across both surveys included logistical challenges with directives; whether wishes would be honored; and the need for public and professional education. Conclusions Professionals and advocates for older persons indicated significant support for the development and use of vaccination‐specific directives. Accordingly, persons who wish to be vaccinated should consider creating such a directive, as it may better advance their wishes and reduce family stress and conflict by providing clear direction in future circumstances—which could include encountering anti‐vaccination sentiment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".