Improving patient‐centred care for persons with Parkinson's: Qualitative interviews with care partners about their engagement in discussions of “off” periods
Bibliographic record
Abstract
OBJECTIVE: This study explored how care partners (CPs) of persons with Parkinson's (PwP) are engaged in discussions of "off" symptoms. METHODS: During qualitative interviews, CPs of PwP sampled by convenience through the Michael J Fox Foundation online clinical trial matching service were asked to describe their familiarity with "off" symptoms, how "off" symptoms were discussed with clinicians, and the impact of "off" symptoms on them. Data were analysed using constant comparative technique by all members of the research team. RESULTS: A total of 20 CPs were interviewed. Compared with PwP, they were more likely to describe "off" symptoms to clinicians. CPs identified important aspects of patient-centred care for PD: establishing a therapeutic relationship, soliciting and actively listening to information about symptoms, and providing self-management support to both PwP and CPs. CPs said that clinicians did not always engage CPs, ask about "off" symptoms or provide self-management guidance, limiting their ability to function as caregivers. CONCLUSION: By not engaging and educating CPs, "off" symptoms may not be identified or addressed, leading to suboptimal medical management and quality of life for PwP. These findings must be confirmed on a broader scale through ongoing research but suggest the potential need for interventions targeted at clinicians and at CPs to promote patient-centred care for PwP.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".