Education Programs for Patients With Parkinson’s Disease Receiving Deep Brain Stimulation: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, presents a significant challenge in terms of both motor and non-motor symptom management. Deep brain stimulation (DBS) emerges as an advanced therapeutic option, showing efficacy in alleviating PD symptoms. However, the literature on educational programs tailored for patients with PD undergoing DBS remains scattered and underexplored. OBJECTIVE: This study sought to map the existing evidence on educational programs designed for patients with PD receiving DBS, highlighting the scope and nature of such interventions. METHODS: Following Joanna Briggs Institute guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 extension, a scoping review was conducted. Relevant documents were identified through PubMed, Scopus, CINAHL, EMBASE, Web of Science, and Google Scholar systematic searches up to November 2024. The review examined content, delivery of educational programs, and outcomes, as well as program characteristics, materials used, and involved health care providers. A narrative synthesis was performed to integrate the findings without specific statistical tests due to the scoping nature of the review. RESULTS: Seven studies were included. Educational interventions demonstrated a positive impact on social adaptation, physical performance, and patient satisfaction. Notably, innovative tools such as the DBS-Edmonton app were identified as beneficial in enhancing patient autonomy and decision-making. CONCLUSIONS: The review underscores the critical role of multidisciplinary, tailored educational interventions in supporting patients with PD undergoing DBS. Despite the promising benefits observed, the field requires further standardization and research to optimize educational strategies and to improve patient outcomes.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".