Personal information documents for people with dementia: Healthcare staff ’s perceptions and experiences
Bibliographic record
Abstract
Person-centred care is internationally recognised as best practice for the care of people with dementia. Personal information documents for people with dementia are proposed as a way to support person-centred care in healthcare settings. However, there is little research about how they are used in practice. The aim of this study was to analyse healthcare staff ’s perceptions and experiences of using personal information documents, mainly Alzheimer’s Society’s ‘This is me’, for people with dementia in healthcare settings. The method comprised a secondary thematic analysis of data from a qualitative study, of how a dementia awareness initiative affected care for people with dementia in one healthcare organisation. The data were collected through 12 focus groups (n = 58 participants) and 1 individual interview, conducted with a range of healthcare staff, both clinical and non-clinical. There are four themes presented: understanding the rationale for personal information documents; completing personal information documents; location for personal information documents and transfer between settings; impact of personal information documents in practice. The findings illuminated how healthcare staff use personal information documents in practice in ways that support person-centred care. Practical issues about the use of personal information documents were revealed and these may affect the optimal use of the documents in practice. The study indicated the need to complete personal information documents at an early stage following diagnosis of dementia, and the importance of embedding their use across care settings, to support communication and integrated care.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".