Vaccine-Related Injuries: Why Canada Needs to Adopt a No-Fault Compensation Scheme in Light of the New H1N1 Vaccine
Bibliographic record
Abstract
On June 11, 2009, the World Health Organization (“WHO”) declared the H1N1 influenza a pandemic. H1N1 is a strain of the influenza virus that, in the past, usually only affected pigs. In the spring of 2009, it emerged in people in North America. This is a new strain of influenza, and because humans have little to no natural immunity to this virus, it can cause serious and widespread illness. As of November 1, 2009, there were more than 440 000 laboratory-confirmed worldwide cases of pandemic influenza H1N1 and over 6000 deaths reported to WHO. In late October, the H1N1 vaccine was approved for rollout across Canada. Since then, Canadians lined up en masse across the provinces and territories to receive the vaccine. This was in part due to the strong urging by the Government of Canada for every Canadian to receive the vaccine. Despite the advantages of wide-scale immunization, there is a significant drawback – many people who receive vaccines each year suffer adverse effects. Despite this fact, Quebec is the only province in Canada that currently has a plan to compensate people who may be injured by vaccinations. For the majority of Canadians, the only recourse when injured by a vaccine is to go through the tort system. By requiring individuals to proceed through the tort system (i.e.: having to prove someone was at fault for causing the injury), many people who have a severe reaction from a vaccine are left with no remedy. This article urges more jurisdictions in Canada to adopt a no-fault compensation scheme for vaccine-related injuries. It will explore how vaccine-related injuries are currently covered under medical malpractice and manufacturer liability schemes, and the reasons why many believe that medical malpractice approaches should be retained in their entirety. In contrast to these beliefs, this article will address how a no-fault system would prove to be an adequate and efficient means of compensating individuals who have been injured by vaccines. This argument will be advanced by looking at how other jurisdictions have implemented no- fault compensation for vaccine-related injuries. Finally, this article will address why it is essential for Canada to adopt this new compensation system as soon as possible in order to address the needs of citizens who may be injured by the new H1N1 vaccine.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 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".