The Balancing Act of Paid Work and Caregiving in Duchenne Muscular Dystrophy (DMD): Results from a Cross-sectional Survey
Bibliographic record
Abstract
INTRODUCTION: Individuals with Duchenne muscular dystrophy (DMD) require increasing care as they age, and caregiver impact can be considerable. The objectives were to investigate the amount of time spent caregiving and working, the extent of adjustments to paid work, and impact on productivity among DMD caregivers in the USA. METHODS: Caregivers of individuals with DMD were recruited through a US-based DMD advocacy group. Measures of impact included time spent on caregiving activities, an overall caregiver impact rating scale ranging from 0 to 10, frequency of work adjustments, and the Work Productivity and Activity Impairment Questionnaire for DMD (WPAI:DMD-CG, v2.0). Survey responses were stratified by care recipient ambulatory status and caregiver employment status. RESULTS: Of 106 caregivers, 82% were mothers; mean (standard deviation) caregiver age was 46 (8.0) years. Eighty-nine percent of respondents reported caring for one individual with DMD and 11% for two individuals with DMD. Sixty-eight percent of respondents were employed. WPAI scores indicated an overall activity impairment of 41%; work time missed was 8%; impairment at work was 31%; work productivity loss was 35%. Across all caregivers, 77% experienced at least one job-related change due to caregiving: 26% took a job with less income potential, 25% quit, 34% changed their role/responsibilities, 29% reduced their hours, and 34% took leave from work to care for their child(ren) with DMD. CONCLUSIONS: This survey extends prior work by providing contemporary, US-specific estimates of work adjustments necessary among caregivers of individuals with DMD. The results demonstrate the considerable impact caregiving has on paid work and productivity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".