Identifying and managing apathy in people with dementia living in nursing homes: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Although apathy is common in people with dementia and has profound negative effects, it is rarely diagnosed nor specifically treated in nursing homes. The aim of this study is to explore experiences in identifying and managing apathy from the perspectives of people with dementia and apathy (PwA), family caregivers (FCs) and professional caregivers (PCs). METHODS: Descriptive qualitative study with purposive sampling, comprising eleven semi-structured in-depth interviews with PwA, FCs or PCs and focus groups with twelve PCs in Dutch nursing homes. Seventeen additional in-depth interviews with caregivers were held, after signals of increasing apathy during the first Covid-19 lockdown. Using an inductive approach, data was analysed thematically to explore the experiences in identifying and managing apathy from the perspective of different stakeholders. RESULTS: Three themes were identified: 1) the challenge to appraise signals, 2) the perceived impact on well-being, 3) applied strategies to manage apathy. Although participants described apathy in line with diagnostic criteria, they were unfamiliar with the term apathy and had difficulties in appraising signals of apathy. Also, the perceived impact of apathy varied per stakeholder. PwA had difficulties reflecting on their internal state. FCs and PCs experienced apathy as challenging when it reduced the well-being of PwA or when they themselves experienced ambiguity, frustration, insecurity, disappointment or turning away. Dealing with apathy required applying specific strategies that included stimulating meaningful contact, adjusting one's expectations, and appreciating little successes. CONCLUSIONS: When addressing apathy in nursing homes, it is important to consider that a) all stakeholders experience that appraising signals of apathy is challenging; b) apathy negatively influences the well-being of people with dementia and especially their FCs and PCs; and c) FCs and PCs can successfully, albeit temporarily, manage apathy by using specific strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".