Truths and Misinformation: A Qualitative Exploration of Myotonic Dystrophy
Bibliographic record
Abstract
BACKGROUND: Myotonic dystrophy (DM1) is an autosomal dominant, progressive, and multisystem condition that impacts affected individuals physically, socially, and emotionally. Understanding individuals' perceptions of their disease is critical to ensuring appropriate information, education, and counseling. METHODS: We conducted a content analysis of findings from a larger study that used a novel, qualitative research approach called photovoice to explore nine patients' experiences of living with DM1. Participants took pictures that illustrated barriers or facilitators to living with DM1; their photographs then formed the basis of semistructured interviews. Transcripts were analyzed and, among themes, we identified one titled "DM1 truths and misinformation" that described participants' disease knowledge. Analysis revealed four categories within this broader theme: "the physical and emotional cost of DM1," "managing my DM1," "genetics and me" and "patients as advocates and educators." RESULTS: Findings showed that DM1 participants had good core knowledge with respect to their disease and its implications. However, each participant held as fact fragments of misinformation that shaped decision-making and pointed to a clear need for strategies to mitigate variable interpretation of health information. CONCLUSIONS: We conclude that there is a need for increased education and awareness about symptoms, genetic information and treatment strategies for patients, their family members, and health care providers.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".