Painted in Broad Strokes: English-Language News Media Coverage of Home Care in Relation to the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
As eldercare was at the forefront of mainstream news media during the COVID-19 pandemic, these media accounts may draw on and/or further reshape public understandings of home care in Canada. A frame analysis informed by critical discourse theory was used to examine 56 English-language articles related to home care (March 2020–March 2021). Home care is often “tacked on” to discussions of long-term residential care and is constructed by what it is not, by what it is a preferred alternative to, and by what it might circumvent (i.e., neglect, contagion). Infused with taken-for-granted meanings and linked to population aging and system crisis, home care is positioned as the progressive future of Canadian eldercare. Although home care investment is a common call, at times the gravity of the problem is imbalanced against small-scale individualistic solutions. Inequities of home spaces and impacts on families are obscured, with homes characterized as idealized places of dignity and (relative) safety. Older adults are positioned as vulnerable, passive victims, in contrast to their benevolent helpers. The authors discuss how we can clarify and strengthen political advocacy and public discourse around eldercare without reinforcing compassionate ageism, apocalyptic demography, and fear of aging while recognizing the nuances around receiving care in either home or residential settings.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".