Stanford Chronic Disease Self-Management Program in myotonic dystrophy: New opportunities for occupational therapists
Bibliographic record
Abstract
BACKGROUND: Chronic disease self-management is a priority in health care. Personal and environmental barriers for populations with neuromuscular disorders might diminish the efficacy of self-management programs, although they have been shown to be an effective intervention in many populations. Owing to their occupational expertise, occupational therapists might optimize self-management program interventions. PURPOSE: This study aimed to adapt the Stanford Chronic Disease Self-Management Program (CDSMP) for people with myotonic dystrophy type 1 (DM1) and assess its acceptability and feasibility in this population. METHOD: Using an adapted version of the Stanford CDSMP, a descriptive pilot study was conducted with 10 participants (five adults with DM1 and their caregivers). A semi-structured interview and questionnaires were used. FINDINGS: The Stanford CDSMP is acceptable and feasible for individuals with DM1. However, improvements are required, such as the involvement of occupational therapists to help foster concrete utilization of self-management strategies into day-to-day tasks using their expertise in enabling occupation. IMPLICATIONS: Although adaptations are needed, the Stanford CDSMP remains a relevant intervention with populations requiring the application of self-management strategies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".