Pandemic Deaths: Media Representations of Long-Term Care in Ontario as a Sociological Case Study
Bibliographic record
Abstract
The mass media influences our worldviews and perceptions, especially of social problems and potential solutions. Importantly, media messages, especially when repeated over time and during a crisis (real or perceived), tend to influence future public policy. Consistent with other periods of crisis and uncertainty, the COVID-19 pandemicization has led to an increased consumption of and reliance on news for accurate information and guidance on what to do and how to act amidst changing public health regulations and social norms. While the aging demographic has made media headlines before the COVID-19 pandemic was declared, the death of nearly 4,000 long-term care facility patients in Ontario alone since March 2020, most of them older adults, has increased the salience of Long-Term Care in the news (television, radio, newspapers, and digital news platforms). In this regard, many claims have been made in the media regarding older adults and their care and safety. But how are the problems leading to mass deaths in LTCFs defined and subsequent solutions presented in the mass media? In order to answer this question, this research asks: how are aging, care, and safety constructed or portrayed in newspaper coverage of LTC in Ontario during the first eight months of the COVID-19 pandemicization? Moreover, what are the implications of these portrayals for an aging population whereby nearly all of us will either need assistance at some point in our lives, provide this assistance to others, or both? Newspaper articles in the National Post on the topic of LTC from March to November 2020 were reviewed using Critical Discourse Analysis. Findings indicate event bias in reporting, journalistic ignorance on the issues in LTC and for those confined therein, dehumanization of older adult subjects, and highly medicalized notions of care and safety.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".