The Experience of Care Partners of Patients with Parkinsonism and Psychosis
Bibliographic record
Abstract
ABSTRACT Background Parkinson’s disease psychosis (PDP) has a major impact on quality of life and care partner burden; however, little is known about the lived experiences of care partners in managing PDP. Objective To understand how care partners of individuals with PDP experience their role and articulate their needs related to psychosis. Methods This was a qualitative study of semi-structured telephone interviews. Recruitment was conducted online via the clinical study matching tool, Fox Trial Finder; study activities took place remotely via telephone interviews. Transcripts of the phone interviews were analyzed by grounded theory methods, and a codebook of key themes that emerged from the analysis was developed. Results Nine care partners (all female) were interviewed. Discussion topics in the codebook included (1) care partner burden and guilt; (2) communication with medical professionals; (3) coping strategies; (4) emotional reactions of the care partner to psychosis; (5) sources of knowledge about PD psychosis; (6) attitudes towards medications for PDP; (7) strategies to care for loved ones with psychosis; (8) psychosis triggers. Conclusions This qualitative analysis uncovers important aspects of the care partner experience, including challenges in navigating the medical system and communicating with professionals. Providers treating patients with PDP should be aware of these constraints and provide added support for strained care partners.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".