“Systems that make humans inhumane”: nursing home care, graphic medicine and Susan MacLeod’s <i>Dying for Attention</i>
Bibliographic record
Abstract
Nursing home care across world have predominantly undergone a progressive marketisation in recent decades, characterised by the dominance of neoliberal values such as productivity, profit motive among others. The looming economic costs and the social-political preferences for neoliberal values have placed the need to provide care for increasing number of older people with complex care needs low on government agendas. As the result, the experiential realities of the stakeholders: care providers, care receivers and their families are often overlooked and undervalued. Susan MacLeod’s Dying for Attention: A Graphic Memoir of Nursing Home Care (2021), besides demonstrating underrepresented and unarticulated of realities of Canadian nursing home care and critiquing the system in place, emphasises the need to reconcile and embrace the long-term care system. Taking cues from the graphic memoir, this article examines how the network of relationships in nursing homes impacts each other and the care provided to the residents. In so doing, the essay brings to light the necessities for coordinated functioning of the system. Utilizing the affordances of comics, the article also examines how evaluating care work based on capitalist standards coerces the care workers to compromise the quality of care provided to the residents.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.063 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".