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Record W4409826762 · doi:10.12927/hcpol.2025.27564

Health Worker Protests in Canada: A Descriptive Analysis of Protest Events from 2021-2022

2025· article· en· W4409826762 on OpenAlexaffvenueabout
Veena Sriram, Meena Rakasi, Kartik Sharma, Michael R. Law, Sorcha A. Brophy

Bibliographic record

VenueHealthcare policy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDescriptive statisticsDescriptive researchPolitical scienceMedia studiesSociologySocial scienceStatistics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.014
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.049
GPT teacher head0.431
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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