Psychometric properties of Arabic-translated-related quality of life scales for people with parkinson disease: a scoping review
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) substantially contributes to poor functional outcomes, loss in productivity, and poor health-related quality of life (HRQoL). Despite the existence of various scales, there is a notable gap in existing HRQoL reviews with regard to the availability of Arabic validated scales. As a response to this gap, the aim of our scoping review is to identify validated scales, focusing on their psychometric validation procedures, to contribute valuable insights to the understanding of HRQoL among the Arabic-speaking people with PD. METHODS: A scoping review was conducted at the end of December 2022, using the Medline and Embase databases. The focus of this review was on examining the psychometric properties and validation procedures of included scales. Articles were included in the full-text screening process if they focused on people with PD of any age, included a scale measuring HRQoL in Arabic, and were written in English, French, or Arabic. RESULTS: After applying inclusion/exclusion criteria, 10 studies were selected to analyze HRQoL scales validated in people with PD. However, the PDQ-39 is the only HRQol PD specific scales validated in the Arabic language. Five studies validated in people with PD were identified in the context of instrument validation (3 generic, 1 specific validated in 2 studies). CONCLUSION: There are several HRQoL measurement scales for people with PD. However, only one specific HRQoL instrument has been validated in Arabic for people with PD. For the remaining instruments identified they were just used in people with PD without being validated in this population.
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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 | 0.040 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".