Personalised goals of people living with dementia and family carers: a content analysis of goals set within an individually tailored psychosocial intervention trial.
Bibliographic record
Abstract
INTRODUCTION: Person-centred goals capture individual priorities in personal contexts. Goal Attainment Scaling (GAS) generates and prioritises valid, personalised outcomes, and has been used in drugs trials involving people living with dementia (PLWD) and is suitable for participants who have heterogeneous outcomes. We used GAS in a care and support intervention trial, administrated by non-clinical facilitators and this study aims to assesses the goals set by PLWD and their family carers.METHODS: We developed training and methodology for non-clinical facilitators to set individualised GAS goals with PLWD and family carer dyads, or family carers alone, as the primary outcome in the intervention trial, between March 2020 and May 2022. We conducted a qualitative content analysis of the goals set to explore participants’ main priorities and unmet needs, and to consider how existing GAS goal domains might be extended in a psychological and support intervention trial context. RESULTS: 302 participating dyads (92% of those consented) collectively set 1043 (mean 3.5, range 3-5) GAS goals. We deductively coded 719 (69%) goals into five existing GAS domains (Mood, Behaviour, Self-care, Cognition, and Instrumental Activities of Daily Living (IADL)); 324 (31%) goals, which focused on carer wellbeing, were coded into new domains: carer break, carer mood, carer behaviour and carer sleep. The most frequently set goals pertained to social support. There was little variation in types of goals set based on who set them (dyad versus family carer alone) or according to the contemporary pandemic-related restrictions.DISCUSSION: It is feasible for people without clinical training to administer Goal Attainment Scaling to set individualised, holistic goals for PLWD and family carers in the community. GAS has potential to increase the relevance of trial outcomes for end users of new dementia interventions, including care and support interventions. This can facilitate the roll out of more personalised care.
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 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 | 0.030 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".