DETERMinants of quality of life, care and costs, and consequences of INequalities in people with Dementia and their carers (DETERMIND): A protocol paper
Bibliographic record
Abstract
OBJECTIVES: DETERMIND (DETERMinants of quality of life, care and costs, and consequences of INequalities in people with Dementia and their carers) is designed to address fundamental, and, as yet unanswered questions about inequalities, outcomes and costs following diagnosis with dementia. These answers are needed to improve the quality of care and equity of access to care, and therefore the quality of life, of people with dementia and their carers. METHOD: DETERMIND is a programme of research consisting of seven complementary workstreams (WS) exploring various components that may result in unequal dementia care: WS1: Recruitment and follow-up of the DETERMIND cohort-900 people with dementia and their carers from three geographically and socially diverse sites within six months following diagnosis, and follow them up for three years. WS2: Investigation of the extent of inequalities in access to dementia care. WS3: Relationship between use and costs of services and outcomes. WS4: Experiences of self-funders of care. WS5: Decision-making processes for people with dementia and carers. WS6: Effect of diagnostic stage and services on outcomes. WS7: Theory of Change informed strategy and actions for applying the research findings. OUTCOMES: During the life of the programme, analysing baseline results and then follow-up of the DETERMIND cohort over 3 years, we will establish evidence on current services and practice. DETERMIND will deliver novel, detailed data on inequalities in dementia care and what drives positive and negative outcomes and costs for people with dementia and carers, and identify factors that help or hinder living well with dementia.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".