A mapping review of good practices of participatory research for an impactful collaboration in disabilities studies
Bibliographic record
Abstract
Abstract Introduction Participatory research is particularly relevant to understanding the challenges faced by people with disabilities (PWDs), as it actively involves them as partners, enabling methodologies to be better adapted to lived realities and producing more relevant and applicable results. By reducing systemic barriers and promoting inclusion, this approach improves understanding and consideration of the specific needs of PWDs in research. Yet, studies have identified hurdles associated with this approach, prompting questions about how organizations portray PWDs, the dynamics among research stakeholders, the distribution of decision-making power, and the actual impact of research on its partners. Aim This study aims to identify the factors that influence the process and results of participatory research in the field of disability studies Methods We conducted a mapping review following the PRISMA-ScR guidelines, and analysis the results according to the input-throughput-outcomes Bergen model Results This study identifies partners skills and training, power sharing and benefits of active involvement as facilitators of participatory research. On the other hand, contextual challenges, and lack of guidance are reported as obstacles. Conclusion This study provides insight into how the various facilitators and obstacles to participatory research and its different processes interact to produce positive, valid and rigorous results.
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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 | 0.137 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".