Barriers and facilitators of communication about off periods in Parkinson’s disease: Qualitative analysis of patient, carepartner, and physician Interviews
Bibliographic record
Abstract
BACKGROUND: Successful patient-physician communication is critical for improving health outcomes, but research regarding optimal communication practices in Parkinson's disease is limited. The objective of the current study was to investigate barriers and facilitators of communication between persons with Parkinson's disease, carepartners, and physicians, specifically in the setting of off periods, with the goal of identifying ways to improve patient-carepartner-physician communication. METHOD: We interviewed persons with Parkinson's, carepartners, and physicians (specialists and non-specialists) using a semi-structured questionnaire to identify and describe experiences, barriers, and facilitators relating to communication about off periods in Parkinson's disease. We used a qualitative descriptive approach to analyze interview transcripts and compare themes between participating groups. RESULTS: Twenty persons with Parkinson's and their carepartners and 20 physicians (10 specialists, 10 non-specialists) participated in interviews. Identified communication barriers included patient-level (e.g. cognitive impairment, reluctance to discuss symptoms), caregiver-level (e.g. caregiver absence), and physician-level (e.g. distraction by technology, lack of appreciation of the burden of off periods) factors. Other barriers included the challenging nature of off periods themselves. Positive physician characteristics such as empathy, respect, and taking time to listen were major facilitators of communication regarding off periods. Persons with Parkinson's, carepartners, and physicians described using various tools (e.g. home diaries, questionnaires, mobile phone videos) to aid communication regarding off periods but participants identified a need for more formal educational materials. CONCLUSIONS: Physicians caring for persons with Parkinson's can improve communication through more patient-centered practice but there is a need for improved educational tools regarding off periods. Further research is needed to identify optimal strategies for communication about off periods and preferred approaches for off period education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".