Healthcare experiences of families affected by Huntington disease: need for improved care
Bibliographic record
Abstract
OBJECTIVES: To explore the healthcare experiences of families affected by Huntington disease (HD), a fatal neurodegenerative genetic disorder, and elicit their suggestions for improvement in the quality of care provided to them. METHODS: 24 semi-structured interviews were completed with members of families affected by HD in Eastern Canada. The sample was chosen to reflect a wide range of experiences with HD (e.g. patients, caregivers, family members at risk, but asymptomatic). RESULTS: Complex needs for healthcare services and emotional supports were found. Participants expressed frustration at the lack of knowledge about HD displayed by their family physicians. They described numerous difficulties accessing appropriate healthcare and other supports, and anticipated access difficulties in the future. Participants offered several suggestions to improve the quality of care to their families, including better education of healthcare professionals about the complex nature of HD and the provision of regular follow-up support. DISCUSSION: Health service planners and policy makers must recognize that HD is a debilitating, complicated illness requiring a high degree of care. Sustained follow-up support from knowledgeable healthcare professionals is required from the initial discovery of HD in the family, throughout a lengthy disease trajectory that normally ends with institutionalization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".