Co-Design to Support the Development of Inclusive eHealth Tools for Caregivers of Functionally Dependent Older Persons: Social Justice Design
Bibliographic record
Abstract
BACKGROUND: eHealth can help reduce social health inequalities (SHIs) as much as it can exacerbate them. Taking a co-design approach to the development of eHealth tools has the potential to ensure that these tools are inclusive. Although the importance of involving future users in the development of eHealth tools to reduce SHIs is highlighted in the scientific literature, the challenges associated with their participation question the benefits of this involvement as co-designers in a real-world context. OBJECTIVE: On the basis of Amartya Sen's theoretical framework of social justice, the aim of this study is to explore how co-design can support the development of an inclusive eHealth tool for caregivers of functionally dependent older persons. METHODS: This study is based on a social justice design and participant observation as part of a large-scale research project funded by the Ministry of Families as part of the Age-Friendly Quebec Program (Québec Ami des Aînés). The analysis was based on the method developed by Miles and Huberman and on Paillé's analytical questioning method. RESULTS: A total of 78 people participated in 11 co-design sessions in 11 Quebec regions. A total of 24 preparatory meetings and 11 debriefing sessions were required to complete this process. Co-designers participated in the creation of a prototype to support the search for formal services for caregivers. The majority of participants (except for 2) significantly contributed to the tool's designing. They also incorporated conversion factors to ensure the inclusiveness of the eHealth tool, such as an adequate level of digital literacy and respect for the caregiver's help-seeking process. In the course of the experiment, the research team's position regarding its role in co-design evolved from a neutral posture and promoting co-designer participation to one that was more pragmatic. CONCLUSIONS: The use of co-design involving participants at risk of SHIs does not guarantee innovation, but it does guarantee that the tool developed will comply with their process of help-seeking and their literacy level. Time issues interfere with efforts to carry out a democratic process in its ideal form. It would be useful to single out some key issues to guide researchers on what should be addressed in co-design discussions and what can be left out to make optimal use of this approach in a real-world context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".