Understanding uncertainty in young-onset Parkinson disease
Bibliographic record
Abstract
Objectives Individuals living with young-onset Parkinson's disease compose a rare subtype of a disease typically associated with older age. Situated within a large grounded theory study exploring information behavior, this paper describes the core category of the theory, i.e. uncertainty. Methods Data were collected with 39 individuals living with young-onset Parkinson's disease who took part in in-depth interviews, focus groups and/or an online discussion board. Fourteen autobiographies written by individuals living with young-onset Parkinson's disease were also used as data sources. Results Through experiencing young-onset Parkinson's disease, participants were confronted with uncertainty along two main lines. First, they experienced uncertainty with respect to their identities as young- and middle-aged adults, deviating from the idealized age-graded life path marked out within their socio-cultural context. Second, they experienced uncertainty with respect to their functioning, as the heterogeneous nature of Parkinson's progression meant that it would not be possible to chart how their disease would change over time. This uncertainty was associated with feelings of lost control over their lives and increased grief. Discussion With a deeper appreciation for how uncertainty is experienced in the lives of those with young-onset Parkinson's disease, health professionals may be better prepared to discuss these issues with patients and provide support and resources.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".