Family-AiD: a family-centred assessment tool in young-onset dementia
Bibliographic record
Abstract
Purpose – Standards of care and care pathways for younger people with dementia vary greatly, making clinical development and service planning challenging. Staff working in dementia services identify that they use biographical knowledge of families to influence clinical decision making. This information is not collected or implemented in a formal manner; highlighting an important knowledge-practice gap. The paper aims to discuss these issues. Design/methodology/approach – The development of a family-centred assessment for use in dementia care has three core components: first, thematic development from qualitative interviews with younger people with dementia and their families; second, clinical input on a preliminary design of the tool; and third, feedback from an external panel of clinical and methodological experts and families living with young-onset dementia. Findings – The 12-item Family Assessment in Dementia (Family-AiD) tool was developed and presented for clinical use. These 12 questions are answered with a simple Likert-type scale to determine areas of unmet need and identify where families may need additional clinical support. Also included is a series of open-ended questions and a biographical timeline designed to assist staff with the collection and use of biographical and family functioning information. Originality/value – A dementia-specific clinical family assessment tool, which also collects background biographical data on family units may be a useful way to document information; inform clinical decision making; and address otherwise unmet needs. Family-AiD has potential to improve clinical care provision of people with dementia and their families. Evaluation of the feasibility and acceptability of its implementation in practice are now required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".