Integration of Conversion Factors for the Development of an Inclusive eHealth Tool With Caregivers of Functionally Dependent Older Persons: Social Justice Design
Bibliographic record
Abstract
BACKGROUND: eHealth can help reduce social health inequalities (SHIs); at the same time, it also has the potential to increase them. Several conversion factors can be integrated into the development of an eHealth tool to make it inclusive: (1) providing physical, technical, and financial access to eHealth; (2) enabling the integration of people at risk of SHIs into the research and development of digital projects targeting such populations (co-design or participatory research); (3) promoting consistency between the digital health literacy level of future users (FUs) and the eHealth tool; (4) developing an eHealth tool that is consistent with the technological skills of FUs; (5) ensuring that the eHealth tool is consistent with the help-seeking process of FUs; (6) respecting the learning capacities of FUs; and (7) being sensitive to FUs' cultural context. However, only little empirical evidence pointing out how these conversion factors can be integrated into an effective eHealth tool is available. OBJECTIVE: On the basis of Amartya Sen's theoretical framework of social justice, the objective of this study was to explore how these 7 conversion factors can be integrated into an eHealth tool for caregivers of functionally dependent older persons. METHODS: This study was based on a social justice design and participant observation as part of a large-scale research project funded by the Ministère de la Famille through the Quebec Ami des Aînés Program. Data were collected by recording the preparation sessions, the co-design and advisory committee sessions, as well as the debriefing sessions. The results were analyzed using Miles and Huberman's method. RESULTS: A total of 78 co-designers participated in 11 co-design sessions, 24 preparation sessions, and 11 debriefing sessions. Of the 7 conversion factors, 5 could be explored in this experiment. The integration of conversion factors has been uneven. The participation of FUs in the development of the tool supports other conversion factors. Respecting the eHealth literacy level of FUs means that their learning abilities and technological skills are also respected because they are closely related to one another and are therefore practically difficult to be distinguished. CONCLUSIONS: Conversion factors can be integrated into the development of eHealth tools that are intended to be inclusive and contribute to curbing SHIs by integrating FU participation into the tool design process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".