“I have to start learning how to live with becoming sick”: A scoping review of the lived experiences of people with Huntington's disease
Bibliographic record
Abstract
Person-centered care (PCC) is recognized as a key component of the delivery of quality healthcare and a model for healthcare systems worldwide. The experience of illness through a person's perspective is one domain defining PCC contributing to a growing interest in examining the lived experiences of illness. This scoping review sought to examine what is known from the existing literature about the lived experiences of persons gene-positive for or living with Huntington's disease (HD) as described in their own voices and to outline prominent psychosocial themes of those experiences. Five databases were systematically searched and analyzed resulting in 19 publications for inclusion. Using a thematic analysis, five prominent psychosocial themes were identified: grappling with control, avoidance as an escape from realities, adaptation to new realities, managing emotions, and appreciation for life. Variation in themes existed across HD life stage of being undiagnosed or diagnosed with HD. The findings of this review demonstrate that individuals who are gene-positive for or living with HD require support well beyond the disclosure of genetic testing and that it may be beneficial for healthcare providers to consider where along the life stage trajectory a person affected by HD may be to ensure the delivery of quality PCC.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".