Adapting Participatory and Creative Online Approaches in Low- and Middle-Income Context: Engaging Children With Physical Disabilities in Mumbai, India
Bibliographic record
Abstract
Participatory and creative methods have gained traction in childhood disability research for eliciting children’s perspectives. However, little is known about how such approaches can be meaningfully adapted to online mode in low- and middle-income contexts (LMICs), where infrastructural, technological, and cultural barriers often restrict participation. Using a case example from Mumbai, where a study on school participation among children with physical disabilities was conducted, we illustrate how photo elicitation and mental map drawing were adapted for online use during the COVID-19 pandemic. Specifically, we demonstrate/discuss: (a) how the combination of such online approaches can be used effectively to support children’s participation in qualitative research; (b) how these approaches were adapted to an online environment in an LMIC setting; (c) possible limitations and challenges that are shaped by LMIC realities; and (d) analytic strategies. By amplifying methodological lessons from Mumbai, this paper contributes practical insights for researchers seeking to meaningfully involve children with disabilities in LMICs and other low-resource settings.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".