Cognitive Interviewing to Develop a New Health-Related Quality of Life Measure for Parkinson's Disease: The Preference-Based Parkinson's Disease Index (PB-PDI)
Bibliographic record
Abstract
Preference-based measures (PBM) for health-related quality of life (HRQoL) are essential in assessing the cost-utility of different treatment options. The preference-based Parkinson's disease Index (PB-PDI) is being developed for people with Parkinson's disease (PD). The aim of this study was to refine the PB-PDI through cognitive interviews with people with PD. Cognitive debriefing was conducted to assess patients' interpretation of items, both in English and French. Participants' feedback guided the iterative modification of the PB-PDI and items were accepted for final inclusion if they were endorsed by three consecutive participants. A total of 16 participants provided feedback on the items, refined the response options, and discussed how to clarify questions. They selected a 2-week timeframe for the PB-PDI recall period. At the end of the cognitive interviews, all seven items and their response options were endorsed in both languages. The cognitive interview process allowed us to refine items and ensure that they were clear in terms of instructions and response options from the perspective of people with PD. The next step will be to elicit preference weights to develop a scoring algorithm and assess its measurement properties.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".