Personalized 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-centered goals capture individual priorities in personal contexts. Goal Attainment Scaling (GAS) has been used in drug trials involving people living with dementia (PLWD) but GAS has been characterized as difficult to incorporate into trials and clinical practice. We used GAS in a trial of New Interventions for Independence in Dementia Study (NIDUS)-family, a manualized care and support intervention, as the primary outcome and to tailor the interventions to goals set. We aimed to assess the feasibility and content of baseline goal-setting. Methods: We developed training for nonclinical facilitators to set individualized GAS goals remotely with PLWD and family carer dyads, or carers alone, in the intervention trial, during the COVID-19 pandemic. A qualitative content analysis of the goals set explored participants' priorities and unmet needs, to consider how existing GAS goal domains might be extended in a psychosocial intervention trial context. Results: Eleven facilitators were successfully trained to set and score GAS goals. A total of 313/328 (95%) participants were able to collaboratively set three to five goals with the facilitators. Of these, 302 randomized participating dyads set 1043 (mean 3.5, range 3 to 5) goals. We deductively coded 719 (69%) goals into five existing GAS domains (mood, behavior, self-care, cognition, and instrumental activities of daily living); 324 (31%) goals were inductively coded into four new domains: carer break, carer mood, carer behavior, and carer sleep. The most frequently set goals pertained to social support. There was little variation in types of goals set based on the context of who set them or level of pandemic restrictions in place. Discussion: It is feasible for people without clinical training to set GAS holistic goals for PLWD and family carers in the community. GAS has potential to facilitate personalization of care and support interventions, such as NIDUS-family, and facilitate the roll out of more personalized care. Highlights: Goal Attainment Scaling (GAS) can capture meaningful priorities of people with dementia and their family carers.A psychosocial intervention RCT used GAS as the primary outcome measure and goals were set collaboratively by non-clinically trained facilitators.The findings underscore the feasibility of using GAS as an outcome measure with this population.The content analysis findings unveiled the diversity in experiences and priorities of the study participants.GAS has the potential to support the implementation of more person-centred approaches to dementia 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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".