Canadian survey on pandemic flu preparations
Bibliographic record
Abstract
BACKGROUND: The management of pandemic influenza creates public health challenges.An ethical framework, 'Stand on Guard for Thee: ethical considerations in pandemic influenza preparedness' that served as a template for the World Health Organization's global consultation on pandemic planning, was transformed into a survey administered to a random sample of 500 Canadians to obtain opinions on key ethical issues in pandemic preparedness planning. METHODS: All framework authors and additional investigators created items that were pilot-tested with volunteers of both sexes and all socioeconomic strata. Surveys were telephone administered with random sampling achieved via random digit dialing (RDD). Eligible participants were adults, 18 years or older, with per province stratification equaling provincial percent of national population. Descriptive results were tabulated and logistic regression analyses were used to assess whether demographic factors were significantly associated with outcomes. RESULTS: 5464 calls identified 559 eligible participants of whom 88.5% completed surveys. Over 90% of subjects agreed the most important goal of pandemic influenza preparations was saving lives, with 41% endorsing saving lives solely in Canada and 50% endorsing saving lives globally as the highest priority. Older age (OR = 8.51, p < 0.05) and current employment (OR = 9.48, p < 0.05) were associated with an endorsement of saving lives globally as highest priority. About 90% of respondents supported the obligation of health care workers to report to work and face influenza pandemic risks excepting those with a serious health condition that increased risks. Over 84% supported the government's provision of disability insurance and death benefits for health care workers facing elevated risk. Strong majorities favored stocking adequate protective antiviral dosages for all Canadians (92%) and, if effective, influenza vaccinations (95%). Over 70% agreed Canada should provide international assistance to poorer countries for pandemic preparation, even if resources for Canadians were reduced. While 92% of this group, believed provision should be 7 to 10% of all resources generated, 43% believed the provision should be greater than 10%. CONCLUSIONS: Results suggest trust in public health officials to make difficult decisions, providing emphasis on reciprocity and respect for individual rights.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".