Process evaluation in a randomised controlled trial of DREAMS-START (dementia related manual for sleep; strategies for relatives) for sleep disturbance in people with dementia and their carers
Bibliographic record
Abstract
INTRODUCTION: DREAMS-START is a multicomponent intervention targeting sleep disturbance in people with dementia. To enhance understanding of the DREAMS-START randomised controlled trial, which showed improved sleep in the intervention compared to the control arm, we conducted a process evaluation exploring (i) DREAMS-START delivery, (ii) behaviour change mechanisms and (iii) contextual factors impacting outcomes. METHODS: Mixed-methods design. We measured intervention adherence, fidelity and additional therapeutic process measures. We interviewed a sub-sample of intervention arm family carers and facilitators delivering DREAMS-START. We analysed data thematically guided by a prespecified theory of change logic model informed by the Theoretical Domains Framework. We measured movement using an actigraph worn by the person with dementia at baseline and at four- and eight-month follow-ups to explore potential mechanisms of action. RESULTS: Attendance was good (82.8% attended ≥4/6 sessions). Mean fidelity score (95.4%; SD 0.08) and median score for all four process measures assessed (5/5; IQR 5-5) were high. We interviewed 43/188 family carers and 9/49 DREAMS-START facilitators. We identified three overarching themes aligned with our model: (i) knowledge and facilitation enable behaviour change, (ii) increasing sleep pressure and developing skills to manage sleep disturbances and (iii) Establishing a routine and sense of control. We were unable to collect sufficient data for pre-specified actigraphy analyses. CONCLUSION: Despite competing demands, carers attended DREAMS-START. It promoted behaviour change through supportive in-session reflection, increasing carer knowledge and skills. This was embedded between sessions and actions were positively reinforced as carers experienced changes. Results will inform future implementation in clinical services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".