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 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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".