COVID-19 Vaccination and Medical Liability: An International Perspective in 18 Countries
Bibliographic record
Abstract
The COVID-19 vaccination has proven to be the most effective prevention measure, reducing deaths and hospitalizations and allowing, in combination with non-pharmacological interventions, the pandemic to be tackled. Although most of the adverse reactions to vaccination present mild symptoms and serious effects are very rare, they can be the cause of legal action against the healthcare workers (HCWs) who administered it. To highlight differences in the medical liability systems, we performed a search for the three most populous countries in each continent on vaccine injury compensation programs, new laws or policies to protect HCWs administering vaccinations introduced during the COVID-19 pandemic, and policies on mandatory vaccinations, on literature databases and institutional sites. We found that in seven countries the medical liability system is based on Common Law, while in eleven it is mainly based on Civil Law. Considering the application of specific laws to protect HCWs who vaccinate during the pandemic, only the USA and Canada provided immunity from liability. Among the countries we analyzed, fourteen have adopted compensation funds. From an international perspective, our results highlight that in eleven (61.1%) countries medical liability is mainly based on Civil Law, whilst in seven (38.9%) it is based on Common Law.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".