Power and agency in health information technology: towards a more meaningful participatory design for sustainable development
Bibliographic record
Abstract
The role of information technology (IT) in improving healthcare has been acknowledged in existing literature. However, implementation of health IT projects in the global South has yielded mixed results. Some projects have failed outright, while others have been wildly successful at improving the effectives and efficiency of healthcare but eventually failed due to decreasing levels of external support. Participatory design has been highlighted as a possible solution to this unsustainable development. However, the few evaluative studies of these projects have indicated that even participatory design does not ensure project sustainability. A rethinking of participatory design is needed if it is to remain a vital aspect of sustainable development. To that end, this paper first reviews several health IT case studies in India to show the reasons for their long-term failure despite using participatory design. These case studies are unique because the client population is community health workers with scant resources and little agency in their day to day routines. The community health workers often have to follow guidelines imposed on them from the top-down despite knowing more about the local situation. This paper argues that participatory design is too often centered on technology and fails to deal with existing issues of disempowerment that health workers may face. Instead, participatory design with the client community must seek to change existing power relationships in order to give them agency in championing the new IT when external support decreases. The concept of lay participatory design is suggested as a possible approach to changing power relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.184 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.009 |
| 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".