A Right to Vote: A Case Study in Nursing Advocacy for Public Policy Reform
Bibliographic record
Abstract
In February 2014, the Government of Canada under Stephen Harper introduced the ‘Fair Elections Act’. This reform to the elections act removed provisions for access to voting for individuals lacking certain forms of identification. Noting that this would have a disproportionate impact on people experiencing homelessness, nursing advocates joined with other activists to try to prevent then subsequently overturn this legislation. The purpose of this paper is to explore the 93rd competency of the College of Nurses of Ontario, “Advocates and promotes healthy public policy and social justice,” by unpacking a case example of advocacy for voting rights. This paper addresses the challenges faced by nurses in doing public policy advocacy and concludes with lessons learned. Fulfilling our college mandated requirement to be politically active means ensuring that public policies are just, equitable, and reflective of the progressive values of Nursing.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.074 | 0.028 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".