Responding the “Wrong Way”: The Emotion Work of Caring for a Family Member With Dementia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although it is generally acknowledged that the changing behaviors of some people living with dementia can be emotionally exhausting for family members, there has been little research on how carers actually interpret and manage their emotional responses when interacting with persons with dementia in context and over time. Applying the concept of emotion work, this analysis examines when and where carers feel they are responding "the right way" to their kin and when and where they resist normative emotions around family care. RESEARCH DESIGN AND METHODS: Semi-structured qualitative interviews (N = 20) and diaries (N = 11) were conducted with, and collected from, family carers in Manitoba, Canada to explore how they negotiate their emotions and emotional displays when caring for a family member whose behaviors are changing. RESULTS: Carers expressed feelings of frustration, anger, and resentment and identified putting on a positive attitude, putting the person with dementia first, protecting the person with dementia, and avoiding conflict and arguing as the "right way" to respond to these feelings. They identified challenges responding the "right way," however, in relation to household chores, and situations that also involved isolation, fear, verbal aggression, and fatigue. DISCUSSION AND IMPLICATIONS: Programs and policies must recognize the complex emotion work of family carers. There is a need for more nuanced education materials, support with household tasks, inclusion of carers' emotional needs in transition planning, and support for carers to exit the caring role when necessary.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".