Picturing the Experience of Living With Myotonic Dystrophy (DM1)
Bibliographic record
Abstract
BACKGROUND: Myotonic dystrophy presents with multisystemic complications, and there is a well-recognized myotonic dystrophy personality profile that is characterized by executive dysfunction, an avoidant personality, and impaired cognition. Understanding symptom impact on patients' lives is crucial for providing appropriate patient-centered care; however, much of the myotonic dystrophy literature reflects the biomedical model, and there is a paucity of articles exploring patient experience. OBJECTIVE: The aim of this study was to use a novel research approach to explore the experiences of patients with myotonic dystrophy. METHODS: Nine individuals participated in a qualitative study using the photovoice method. Photovoice uses the visual image to document participants' lives, and participants took pictures pertaining to living with myotonic dystrophy that stimulated individual and focus group interviews. We used content analysis to analyze the data; in turn, codes were collapsed into themes and categories. Findings were presented to participants to ensure resonance. RESULTS: Participants took 0-40 photographs that depicted barriers and facilitators to living successfully with myotonic dystrophy. We identified two categories that include participants' challenges with everyday activities, their worries about the future, their grief for lost function and social opportunities, and their resilience and coping strategies. Participants also described their experiences using the photovoice method. CONCLUSION: Photovoice is a useful approach for conducting research in myotonic dystrophy. Participants were active research collaborators despite perceptions that individuals affected with myotonic dystrophy are apathetic. Our findings suggest that participants are concerned about symptom impact on reduced quality of life, not symptoms that clinicians preferentially monitor. Nurses, therefore, are essential for providing patient-centered, holistic care for patients' complex biopsychosocial needs. Research exploring current physician-led clinical care models is warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".