Nursing Voices during COVID-19: An Analysis of Canadian Media Coverage
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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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".