Priority setting of ICU resources in an influenza pandemic: a qualitative study of the Canadian public's perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Pandemic influenza may exacerbate existing scarcity of life-saving medical resources. As a result, decision-makers may be faced with making tough choices about who will receive care and who will have to wait or go without. Although previous studies have explored ethical issues in priority setting from the perspective of clinicians and policymakers, there has been little investigation into how the public views priority setting during a pandemic influenza, in particular related to intensive care resources. METHODS: To bridge this gap, we conducted three public town hall meetings across Canada to explore Canadian's perspectives on this ethical challenge. Town hall discussions group discussions were digitally recorded, transcribed, and analyzed using thematic analysis. RESULTS: Six interrelated themes emerged from the town hall discussions related to: ethical and empirical starting points for deliberation; criteria for setting priorities; pre-crisis planning; in-crisis decision-making; the need for public deliberation and input; and participants' deliberative struggle with the ethical issues. CONCLUSIONS: Our findings underscore the importance of public consultation in pandemic planning for sustaining public trust in a public health emergency. Participants appreciated the empirical and ethical uncertainty of decision-making in an influenza pandemic and demonstrated nuanced ethical reasoning about priority setting of intensive care resources in an influenza pandemic. Policymakers may benefit from a better understanding the public's empirical and ethical 'starting points' in developing effective pandemic plans.
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.
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 | 0.013 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it