Impact of mobile health in diabetic retinopathy awareness and eye care behavior among Indigenous women
Bibliographic record
Abstract
BACKGROUND: Diabetes is increasingly widespread among Indigenous people, and diabetic retinopathy (DR) is a diabetes eye complication and a common cause of vision loss among adults in Canada. Indigenous women have a high risk of diabetes which increases their risk for DR. This study explored utilizing mobile health (mHealth) via text messages to provide DR awareness and improve diabetic-eye care behavior. This study identified the changes in DR awareness and eye care behavior due to a mHealth education intervention among Indigenous women with or at risk of diabetes. METHODS: A pre-post study which adopted an embedded concurrent mixed methods approach guided by self-determination theory and the medicine wheel. Study participants were First Nations and Métis women living with or at risk of diabetes in Saskatoon, Canada. Data was collected via sharing circles and a DR knowledge, attitude, and practice survey. Pre-intervention participants' baseline information on DR knowledge and behavior were obtained from participants. After that, participants received daily text messages on diabetes-eye related information for 12 weeks. Post-intervention, the impact of the mHealth intervention on DR awareness and eye care behavior was assessed. RESULTS: Following the intervention, the DR knowledge, attitude, and practice scores significantly improved. Individuals living with diabetes had increased DR attitude and practice post-scores compared to those at risk of diabetes. Older women had a lower pre-post change in practice scores compared to younger women. The mHealth intervention provided a holistic approach to support diabetes-eye care and empowered the study participants to eat healthily, take medication as prescribed, and have regular medical and eye check-ups. CONCLUSIONS: The mHealth education intervention increased DR awareness and fostered a change in diabetes-eye care behavior. Health information via text messaging can motivate, provide support, and empower individuals as well as prevent and manage chronic conditions and reduce the risk of complications.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".