Fears and uncertainties of people with Parkinson's disease
Bibliographic record
Abstract
BackgroundThe patient experience of Parkinson's disease (PD) is heterogeneous, with limited prognostic tools to predict individual outcomes, leading to significant uncertainty for people with PD. Under-recognition of both psychosocial and biological drivers of fear and uncertainty in Parkinson's disease (PD) by clinicians may further contribute to patient distress.ObjectiveThe objective of the present study is to investigate fear and uncertainty in people with PD.MethodsIn-depth interviews were conducted with twenty people with PD (11 semi-structured, 9 guided/prompted). Thematic analysis organized the fears/uncertainties by topic as well as by contextual factors such as the timing of the fear (e.g., active or anticipatory; at the time of diagnosis or developed subsequent to diagnosis) and the lexicon used to describe it.ResultsParticipants expressed a wide range of fears and uncertainties about their future and quality of life with PD, which shifted with disease progression. Most fears were anticipatory rather than in response to current concerns. Participants reported substantial psychosocial influence from media personalities or family/friends with PD. Most participants reported that they had not disclosed their fears to their healthcare providers.ConclusionsClinicians caring for people with PD should be aware of a range of often-unspoken fears and uncertainties, which may carry a substantial psychosocial burden. Open acknowledgement and normalization by clinicians may help patients feel less isolated in their disease.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".