Tailoring and evaluating an intervention to improve shared decision-making among seniors with dementia, their caregivers, and healthcare providers: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of Alzheimer's disease and other forms of dementia raises new challenges to ensure that healthcare decisions are informed by research evidence and reflect what is important for seniors and their caregivers. Therefore, we aim to evaluate a tailored intervention to help healthcare providers empower seniors and their caregivers in making health-related decisions. METHODS: In two phases, we will: (1) design and tailor the intervention; and (2) implement and evaluate it. We will use theory and user-centered design to tailor an intervention comprising a distance professional training program on shared decision-making and five shared decision-making tools dealing with difficult decisions often faced by seniors with dementia and their caregivers. Each tool will be designed in two versions, one for clinicians and one for patients. We will recruit 49 clinicians and 27 senior/caregiver to participate in three cycles of design-evaluation-feedback of each intervention components. Besides think-aloud and interview approaches, users will also complete questionnaires based on the Theory of Planned Behavior to identify the factors most likely to influence their adoption of shared decision-making after exposure to the intervention. We will then modify the intervention by adding/enhancing behavior-change techniques targeting these factors. We will evaluate the effectiveness of this tailored intervention before/after implementation, in a two-armed, clustered randomized trial. We will enroll a convenience sample of six primary care clinics (unit of randomization) in the province of Quebec and recruit the clinicians who practice there (mostly family physicians, nurses, and social workers). These clinics will then be randomized to immediate exposure to the intervention or delayed exposure. Overall, we will recruit 180 seniors with dementia, their caregivers, and their healthcare providers. We will evaluate the impact of the intervention on patient involvement in the decision-making process, decisional comfort, patient and caregiver personal empowerment in relation to their own healthcare, patient quality of life, caregiver burden, and decisional regret. DISCUSSION: The intervention will empower patients and their caregivers in their healthcare, by fostering their participation as partners during the decision-making process and by ensuring they make informed decisions congruent with their values and priorities. TRIAL REGISTRATION: ClinicalTrials.org, NCT02956694 . Registered on 31 October 2016.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".