Engaging Nursing Voice and Presence During the Federal Election Campaign 2015
Bibliographic record
Abstract
During the Canadian federal election in 2015, we conducted a systematic inquiry into the methods and messages developed by national nursing organizations to communicate their policy platforms and their strategies for member and public engagement. Throughout the campaign and in the post-election period, the nursing organizations presented an outward-looking view to improve health and healthcare for Canadians. We observed ways in which they adopted a nursing lens on the issues by showcasing background research, by drawing on relevant nursing knowledge and by communicating clear policy messages based on nursing expertise. The organizations and their members were effective in using social media as a primary tool for reaching out to the candidates, the public and the opinion leaders. The increasing engagement of nursing students in political action is noted as a promising sign for the future impact of the profession. Although the nursing presence was visible in this election, healthcare did not become a strong issue for the public and the political parties. We include a section on post-election uptake of issues raised during the campaign. We conclude with a call for a policy research agenda that deepens our knowledge of political advocacy with a view to identifying how patterns of engagement are defining nursing's collective influence and contributions to health equity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".