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Record W4399500094 · doi:10.1080/21504857.2024.2363459

“Systems that make humans inhumane”: nursing home care, graphic medicine and Susan MacLeod’s <i>Dying for Attention</i>

2024· article· en· W4399500094 on OpenAlexaboutno aff
Livine Ancy A, Sathyaraj Venkatesan

Bibliographic record

VenueJournal of Graphic Novels & Comics · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsNursingPsychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.289
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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