An exploratory analysis of global trends in wheelchair service provision knowledge across different demographic variables: 2017–2020
Bibliographic record
Abstract
To explore global trends in manual wheelchair service provision knowledge across geographic, professional, and socioeconomic domains. A secondary analysis of a dataset from the International Society of Wheelchair Professionals' Wheelchair Service Provision Basic Knowledge Test was conducted. The dataset included test takers from around the world and was extracted from Test.com and International Society of Wheelchair Professionals' Wheelchair International Network. Participants 2,467 unique test takers from 86 countries. Interventions Not applicable. International Society of Wheelchair Professionals' Wheelchair Service Provision Basic Knowledge Test. We identified significant inverse associations between pass rate and the following variables: education (high school and some college), test taker motivation (required by academic program or employer), and country income setting (low and middle). There were significant positive associations between pass rate and the following variables: training received (offered by Mobility India or 'other NGO'), and age group served (early childhood). Global wheelchair knowledge trends related to key variables such as training, occupation, and income setting have been preliminarily explored. Future work includes further validation of the primary outcome measure and recruitment of a larger sample size to further explore significant associations between additional test taker variables.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".