Despite all our rage: An autoethnographic analysis on the role of shared affect in dementia caregiving relationships
Bibliographic record
Abstract
Using an autoethnographic approach, this paper explores the phenomenon of shared rage between Alzheimer’s patients and their informal family caregivers. Unlike previous analyses regarding dementia care, this work understands that rage within caregiving relationships is both dynamic and productive . Drawing broadly from social scientific studies regarding emotional labour and “feeling work,” this work argues that Alzheimer’s sufferers and their informal caregivers form two halves of a dyad, each of whom may use rage as a form of protection against loss of relational identity and pursuant grief, and to demand humane and dignified treatment from broken formal care systems. This individual rage simultaneously offers a point of connection between both halves of the caregiving dyad, which is otherwise torn asunder by interpersonal manifestations of the disease. Ultimately this paper argues for a brave, curious and compassionate response to caregiving dyads in which experiences of rage are not stigmatized, minimized or medicalized. Rather, this analysis suggests that experiences of anger are recognized as an often-excruciating form of emotional labour necessitated by an insidious disease and inadequate formal care systems. • Emotional labour in dementia may be understood as dyadic: shared between caregiver and recipient • Within this dyad, rage is a shared affective response to the corrosive force of dementia • Rage may be understood a productive force in terms of managing grief and broken formal care systems • Autoethnography allows a window into the experience of shared affect within a caregiving dyad
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".