531 (mis)using randomised controlled trials as a hegemonic weapon: the case of mandatory influenza vaccination for healthcare workers in canada
Bibliographic record
Abstract
Introduction In 2013, British Columbia, Canada, instituted a Policy requiring healthcare workers (HCWs) to accept influenza vaccination or wear a mask at work throughout the influenza season. The Policy’s stated objectives (prevent influenza transmission to vulnerable patients; reduce influenza morbidity and mortality; and reduce worker absenteeism) did not refer to the health of HCWs. Moreover, the four randomised controlled trials (RCTs) cited as evidence supporting this influenza vaccine-or-mask policy were misinterpreted (or misrepresented) by its proponents, which, we argue, not only threatens the health of workers, the public and patients, but jeopardises the credibility of public health institutions. Methods Plausibility of the four RCT findings attributing indirect patient benefits to HCW influenza vaccination were assessed by international experts comparing percentage reductions in patient risk reported by the RCTs to predicted values; we synthesise the results of the analysis and discuss the political factors that may explain the (mis)use of the RCT evidence. Result Each RCT violated the basic mathematical principle of dilution by reporting greater percentage reductions with less influenza-specific patient outcomes and/or patient mortality reductions exceeding even favourably derived predicted values by at least 6–15-fold. Contextual factors more likely to explain the RCT results were ignored. The prioritisation of quantitative data masks the economic and political agendas of policy makers. Discussion This policy is a case of (mis)use of RCT evidence as a weapon against workers while transferring large amounts of public funds to a questionable program and ultimately to pharmaceutical companies. We argue that worker acceptance of influenza vaccination should be voluntary, and public resources be more appropriately allocated to measures more likely to result in greater public health benefit, such as improved sick leave to encourage ill workers to stay home, or more staffing to allow HCWs to be more vigilant with infection control procedures.
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 | 0.584 | 0.817 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".