Wheelchair service provision training during armed conflict: preliminary results from a pre-post study in Ukraine
Bibliographic record
Abstract
Background: Universal Health Coverage (UHC) cannot be achieved without equitable access to assistive technology (AT). Wheelchairs are among the most needed AT products worldwide, yet service provision is hindered by limited workforce capacity, inadequate training, and fragile supply systems, challenges that become critical in conflict and emergency settings. Objective: This study aimed to evaluate the effectiveness of a World Health Organization (WHO)- supported wheelchair service training program in Ukraine, developed in partnership with the International Society of Wheelchair Professionals (ISWP), in improving theoretical knowledge, wheelchair skills performance and confidence, among rehabilitation professionals. Methods: A five-day, 40 h training program based on the WHO Wheelchair Service Training Package-Basic Level (WSTPb) was delivered to 39 rehabilitation professionals in Ukraine. Training combined theoretical instruction, hands-on skill practice, and adapted educational strategies, including group-based ISWP testing, to overcome infrastructure constraints. Pre- and post-training assessments were conducted using the Wheelchair Skills Test-Questionnaire (WST-Q). Results: < 0.01). Importantly, rehabilitation assistants demonstrated the largest relative improvement, reducing pre-training disparities with occupational and physical therapists. Conclusion: The findings highlight how targeted educational interventions can expand the AT workforce, promote equitable skill acquisition across professional cadres, and strengthen AT integration into UHC, even in the context of armed conflict. The Ukrainian experience illustrates both the clinical challenges, such as mastering advanced wheelchair skills, and the educational challenges including addressing diverse professional backgrounds and limited infrastructure, that are inherent in AT service provision. This model can inform future workforce capacity-building strategies for AT in both emergencies and routine health system strengthening efforts.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".