Co-creation of a family-focused service model living with younger onset dementia
Bibliographic record
Abstract
Families living with younger onset dementia face unique social challenges, which services do not currently address. There is a gap in understanding the experiences of families affected by younger onset dementia, including the interactions and relationships within the family units, and with existing services and supports. In this qualitative research study, semi-structured interviews were conducted across Australia with seven health and social care providers, five persons living with younger onset dementia, six spouse/carers, and 17 children. A thematic analysis was conducted using a theoretical lens drawn from the combined perspectives of the social model of disability and family systems-illness model. Two central themes emerged in the data: (1) Understanding the social demands on the family – lifecycle challenges living with younger onset dementia from a social and family context; and (2) Interactions of health and social care providers with families living with younger onset dementia – opportunities and challenges in providing support and services geared to a whole family approach. In order to provide families for those living with younger onset dementia with optimal assistance and support, co-creation of a family-focused services model is proposed. Its purpose is to support effective therapeutic interactions and service development among service users, service providers, and stakeholders.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".