Towards Capacity Building in Wheelchair Service: A Participatory Action Research Approach to Develop an International Package for Wheelchair Service Education
Bibliographic record
Abstract
Research findings over several years and across the globe have demonstrated that education and training for individuals who serve on the front line of wheelchair service provision is inadequate. It is a problematic situation that challenges equitable access to an appropriate wheelchair for the 80 million wheelchair users worldwide. It is a situation that requires social change initiated by those in the field. As a methodology whose objective it is to co-create knowledge and promote social change, participatory action research (PAR) was a natural choice for the creation of the Wheelchair Educators’ Package (WEP). This paper describes our use of a 10-step PAR process combined with the Participation Choice Points model to actively recruit and engage a 32-member team of community partners affected by this situation to develop the WEP. As a team representing the interests of the ultimate beneficiaries of wheelchair service providers (i.e., wheelchair users), our members were carefully chosen to contribute to a WEP that would be of use to educators in diverse contexts across the globe but not all members were trained in research. The PAR process, designed to optimize and support participation at the desired level of our team members, as well as the measurement of team member engagement, experience, and level of satisfaction with the PAR process are described in this paper. We also offer reflections on the use of our methods and describe how this work provides insights into the way in which academic and global partners may co-create an environment that empowers community partners, many of whom volunteer their time to complete works such as the WEP.
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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 | 0.083 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".