Capturing patients’ experiences to change Parkinson’s disease care delivery: a multicenter study
Bibliographic record
Abstract
Capturing patients' perspectives has become an essential part of a quality of care assessment. The patient centeredness questionnaire for PD (PCQ-PD) has been validated in The Netherlands as an instrument to measure patients' experiences. This study aims to assess the level of patient centeredness in North American Parkinson centers and to demonstrate the PCQ-PD's potential as a quality improvement instrument. 20 Parkinson Centers of Excellence participated in a multicenter study. Each center asked 50 consecutive patients to complete the questionnaire. Data analyses included calculating case mix-adjusted scores for overall patient centeredness (scoring range 0-3), six subscales (0-3), and quality improvement (0-9). Each center received a feedback report on their performance. The PCQ-PD was completed by 972 PD patients (median 50 per center, range 37-58). Significant differences between centers were found for all subscales, except for emotional support (p < 0.05). The information subscale (mean 1.62 SD 0.62) and collaboration subscale (mean 2.03 SD 0.58) received the lowest experience ratings. 14 centers (88 %) who returned the evaluation survey claimed that patient experience scores could help to improve the quality of care. Nine centers (56 %) utilized the feedback to change specific elements of their care delivery process. PD patients are under-informed about critical care issues and experience a lack of collaboration between healthcare professionals. Feedback on patients' experiences facilitated Parkinson centers to improve their delivery of care. These findings create a basis for collecting patients' experiences in a repetitive fashion, intertwined with existing quality of care registries.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".