A Patient-Based Needs Assessment for Living Well with Parkinson Disease: Implementation via Nominal Group Technique
Bibliographic record
Abstract
Background. Parkinson's disease (PD) is a neurodegenerative condition with complex subtleties, making it challenging for physicians to fully inform their patients. Given that approximately 50% of Americans access the Internet for health information, the development of a multimedia, web-based application emphasizing targeted needs of people with Parkinson's disease (PwP) has the potential to change patient's lives. Objectives. To determine what information PwP perceive could enhance their quality of life. Methods. Group sessions utilizing nominal group technique (NGT) were conducted. Participants were asked "what information do you want to know about that would help you live well with PD?" Silent generation of ideas preceded discussion followed by anonymous ranking of items. A "summary score" (sum of rank × frequency) was calculated. Results. 36 individual items were collapsed into 9 categories. Coping with emotions, changing relationships, and social implications of PD were ranked as most important. Financial supports and skills for self-advocacy were also highly ranked. Conclusions. Qualitative research methodology was utilized to determine the unmet needs of PwP. Results of this survey will inform the development of a patient-oriented, online resource, the goal will be to provide information and strategies to improve symptom management, reduce disability and address all relevant concerns important to those affected by PD.
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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 | 0.037 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".