Health Worker Protests in Canada: A Descriptive Analysis of Protest Events from 2021-2022
Bibliographic record
Abstract
Objectives: We analyzed protest events undertaken by health workers in Canada in 2021 and 2022. Our analysis focused on the quantity and distribution of protests within Canada, policy demands expressed by organizers and the temporal sequence of protest events. Methods: Our data came from the Armed Conflict Location and Event Data (ACLED) project, which includes a dataset with all health worker-involved protest events in specific jurisdictions, including Canada. Using an existing taxonomy of policy demands for protest events, we analyzed specific types of protests, protest demands and temporal trends. Results: = 19). Conclusion: Canadian health workers expressed concerns on policy issues ranging from opposition to COVID-19 mitigation to underinvestment in health systems. Identifying and recognizing these drivers and developing targeted policy to address them through inclusive and sustained engagement with health workers will contribute to long-term solutions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".