Using letter grades as descriptors on visual analogue scales evaluating mobility devices in low- and middle-income countries
Bibliographic record
Abstract
The World Health Organization estimates that over one billion people, approximately 15% of the world’s population, have a disability necessitating a mobility device, of whom eighty percent live in low-income countries (1). In low- and middle-income countries (LMIC), provision and/or funding of mobility devices is complicated by the lack of government regulations, limited resources, increased environmental challenges, and inaccessible infrastructure (2). The high demand for mobility devices puts pressure on the providers to produce low-cost devices (3). To aid in the attempt to balance quality and cost, organizations funding, manufacturing or purchasing mobility devices request anecdotal user feedback via patient reported outcome measures (PROM) to enable more effective use of limited funds (4). The purpose of this study is to validate Rispin and team’s novel addition of letter grades as descriptors to visual analogue scales (VAS) used within questionnaires evaluating mobility devices. The hypothesis was that school grades would be a valid and reliable universal descriptor that would enable clinicians in LMICs to share information with manufacturers in a more meaningful way. Rispin and team conducted three studies in Kenya with three questionnaires using grades as VAS descriptors: the Lower Limb Function Questionnaire (LLFQ), Aspects of Wheelchair Mobility Test (AWMT), and Wheelchair Components Questionnaire for Condition (WCQc). In a retrospective analysis, applicable data from each study were extracted to assess the reliability and validity of this scaling format using intra-class correlation coefficients (ICC) and t-tests respectively. The VAS with grades demonstrated acceptable test-retest reliability for the LLFQ (ICC=0.74), and excellent intra-rater reliability for the WCQc (ICC=0.86). Concurrent validity was strong in evaluation of grade versus numerical scores in the LLFQ (r= 0.90, p<0.0001). The AWMT discriminated between ground conditions (mobility on rough versus smooth terrain) in the Motivation wheelchair (t(32)= 2.68, p=0.01) and Whirlwind wheelchair (t(32)= 4.14, p=0.0002), and between the two chairs with the Motivation showing greater maneuverability (t(32)= 3.19, p=0.003). In conclusion, the use of grades as descriptors on a VAS was a reliable and valid format in these test conditions, and has crucial applicability for rehabilitation strategies in LMIC.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".