Perceptions on mobile health use for health education in an Indigenous population
Bibliographic record
Abstract
Introduction: Indigenous peoples in Canada face numerous health needs and challenges and often have poor health status due to inequitable access to care. Providing culturally appropriate support for health conditions, particularly chronic conditions that require self-management, can assist in averting complications and morbidity. Mobile health is a useful medium for delivering health education across different populations. However, meaningful user involvement is necessary because mobile health interventions suitable for one population may not be appropriate for another. Indigenous people's views will inform the use of mobile health interventions in Indigenous communities. Objective: The study explored the perception of Indigenous women on using mobile health as a tool for receiving health information. Methods: This was a qualitative study, and participants comprised of 22 Indigenous women (First Nations and Métis) with or at risk of diabetes, aged 18-69 years in Saskatoon, Canada. After 12 weeks of disseminating diabetic eye care information via text messaging, data were collected via sharing circle discussions and analyzed using thematic analysis. Results: Participants indicated that the nature of messages such as the use of Indigenous languages, the message content, frequency of messages, group activities, and delivery formats such as voice messages, mobile applications, Internet, two-way messaging, and text messages were essential considerations in using mobile health as a tool for receiving health information. Conclusion: Different factors need to be considered in using mobile health as a tool for health education among Indigenous peoples. These factors could be applicable in implementing mobile health solutions in other populations for the management of health conditions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".