Carers perspectives on the activity patterns of people with dementia
Bibliographic record
Abstract
Participation in activity is essential for the psychological well-being of people with dementia. The potential benefits of home-based activity programmes may depend on family carers, but little is known about their experience. This study aimed to elicit carers' experiences of involving the person with dementia in activity. Thirty in-depth interviews (i.e. initial and follow-up) were carried out with 15 co-resident carers of people with dementia who were recruited through local community mental health teams. Data were analysed using a grounded theory method. Overall, findings from initial interviews were taken back to the participants at the follow-up interviews. Five activity patterns were identified, which ranged from their usual activity patterns along a continuum through recognizable, illogical, irresponsible and finally reaching a dispossessed pattern. Carers used particular strategies and experienced particular emotional responses along this continuum. This work highlights the complex, temporal and dynamic nature of family carers' involvement in activity engagement. Clinician's interventions could be enhanced by: (1) recognizing the long-term experience of carers in decision making; (2) understanding the strategies used; (3) allowing carers to talk through and share their experiences in a non-judgmental way; (4) ensuring that carers are happy with any suggested interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".