Nursing Voices during COVID-19: An Analysis of Canadian Media Coverage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While it is generally recognized that nurses and nursing issues are underrepresented in the media, the contrary is also true during major public health care crises like Ebola and SARS (Severe Acute Respiratory Syndrome). We see this phenomenon unfolding in the midst of the current COVID-19 pandemic with nurses and nursing issues receiving extensive media coverage in Canada and internationally. To gain more insights into this media coverage, we analyzed the content of Canadian news stories published in both English and French during the first five months of the COVID-19 pandemic. This paper presents the findings of our analysis and identifies important lessons learned. We believe that our findings serve as an important starting point for understanding nurses’ agency and the media savviness they displayed during the first months of the pandemic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it