How to implement person-centred care and support for dementia in outpatient and home/community settings: Scoping review
Bibliographic record
Abstract
BACKGROUND: Little prior research focused on person-centred care and support (PCCS) for dementia in home, community or outpatient care. We aimed to describe what constitutes PCCS, how to implement it, and considerations for women who comprise the majority of affected persons (with dementia, carers). METHODS: We conducted a scoping review by searching multiple databases from 2000 inclusive to June 7, 2020. We extracted data on study characteristics and PCCS approaches, evaluation, determinants or the impact of strategies to implement PCCS. We used summary statistics to report data and interpreted findings with an existing person-centred care framework. RESULTS: We included 22 studies with qualitative (55%) or quantitative/multiple methods design (45%) involving affected persons (50%), or healthcare workers (50%). Studies varied in how PCCS was conceptualized; 59% cited a PCC definition or framework. Affected persons and healthcare workers largely agreed on what constitutes PCCS (e.g. foster partnership, promote autonomy, support carers). In 4 studies that evaluated care, barriers of PCCS were reported at the affected person (e.g. family conflict), healthcare worker (e.g. lack of knowledge) and organizational (e.g. resource constraints) levels. Studies that evaluated strategies to implement PCCS approaches were largely targeted to healthcare workers, and showed that in-person inter-professional educational meetings yielded both perceived (e.g. improved engagement of affected persons) and observed (e.g. use of PCCS approaches) beneficial outcomes. Few studies reported results by gender or other intersectional factors, and none revealed if or how to tailor PCCS for women. This synthesis confirmed and elaborated the PCC framework, resulting in a Framework of PCCS for Dementia. CONCLUSION: Despite the paucity of research on PCCS for dementia, synthesis of knowledge from diverse studies into a Framework provides interim guidance for those planning or evaluating dementia services in outpatient, home or community settings. Further research is needed to elaborate the Framework, evaluate PCCS for dementia, explore determinants, and develop strategies to implement and scale-up PCCS approaches. Such studies should explore how to tailor PCCS needs and preferences based on input from persons with dementia, and by sex/gender and other intersectional factors such as ethnicity or culture.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".