A trial platform to develop a tailored theory-based intervention to improve professional practice in the disclosure of a diagnosis of dementia: Study protocol [ISRCTN15871014]
Bibliographic record
Abstract
BACKGROUND: For people with dementia, care should include an explanation of the diagnosis to individuals and their carers, and information about the likely prognosis and possible packages of care. However, this is neither routine nor inevitable, and there is wide variation in the practice of disclosure. The aim of this study is to develop a tailored theory-based intervention to promote appropriate disclosure of diagnosis of dementia. METHODS: There are three objectives. Objective 1 is to define and develop an appropriate model of disclosure; this will be addressed using a multidisciplinary consensus development process. Objective 2 is to identify factors that influence disclosure of diagnosis; a questionnaire based upon theoretical constructs from a range of behavioural theories will be developed and members of old age mental health teams will be surveyed. The analysis will identify those factors that best predict intention to disclose a diagnosis to a person with dementia. Objective 3 is to develop and pilot test a theory-based intervention to promote disclosure of diagnosis that targets attitudes, beliefs and actions most amenable to change. Objective 3 will use the results of Objectives 1&2 to design and pilot test an intervention to improve the process of and increase the proportion of individuals receiving a diagnosis of dementia, for members of old age mental health teams. This work will lead to a proposal for a randomised controlled trial of the intervention.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".