Understanding acceptability of digital health technologies among francophone-speaking communities across the world: a meta-ethnographic study
Bibliographic record
Abstract
Introduction Increasingly, people are turning toward digital health technologies to support their care management, communication with health professionals, and performing activities of daily living. Digital health technologies may be well implemented in clinical practices in several jurisdictions, but the influence of sociocultural factors may sometimes be neglected. To increase use and sustainability of these innovative solutions in health care, we need to understand acceptability among diverse groups of the population such as linguistically diverse populations. Francophone-speaking populations in Canada, for example, are known to endure challenges with income, health and difficulties associated with living in rural areas which impede on their likelihood to use digital health technologies. As part of the University of Ottawa International Francophonie Research Chair on Digital Health Technologies, this study aimed to understand the conditions that make digital health technologies acceptable among francophone-speaking communities. Methods Using a meta-ethnography methodology, this study synthesizes international qualitative research on social acceptability of digital health technology among francophone-speaking communities. We focused on four types of digital health technologies: telemedicine, mobile technologies, wearable technologies, and robotic technologies. Using Noblit and Hare's 7 phase approach to conducting a meta-ethnography, we were able to get a comprehensive synthesis and understanding of the research landscape on the issue. Studies published between 2010 and 2020 were included and synthesized using NVivo, excel and a mind mapping technique. Results Our coding revealed that factors of social acceptability for digital health technologies could be grouped into the following categories: care organization, self-care support, communication with care team, relational and technical risks, organizational factors, social and ethical values. Our paper discusses the themes evoked in each category and their relevance for the included digital health technologies. Discussion In discussing the results, we present commonalities and differences in the social acceptability factors of the different digital health technologies. In addition, we demonstrate the importance of considering sociocultural diversity in the study of social acceptability for digital health technologies. Implications The results of this study have implications for practitioners who are the instigators of digital health technology implementation with healthcare service users. By understanding factors of social acceptability among francophone-speaking communities, practitioners will be better suited to propose and support the implementation of technologies in ways that are suitable for these individuals. For policymakers, this knowledge could be used for developing policy actions based on consideration for diversity.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".