IMPACT OF MOBILE HEALTH (MHEALTH) IN DIABETIC RETINOPATHY (DR) AWARENESS AND EYE CARE BEHAVIOR AMONG INDIGENOUS WOMEN
Bibliographic record
Abstract
Diabetes is increasingly prevalent among Indigenous people and diabetic retinopathy (DR) is an eye complication of diabetes, and a common cause of blindness among adults in Canada. Indigenous women have a high risk of diabetes likewise increasing their risk for DR. The study examined factors that motivate and constrain Indigenous women from adopting healthy eye care behaviors and identified the changes in DR awareness and eye care behavior as a result of a mHealth education intervention among adult Indigenous women with diabetes or at-risk of diabetes (n=78). This was a pre-post-study which adopted an embedded concurrent mixed methods approach guided by self-determination theory and the medicine wheel. Pre-intervention DR awareness and eye care behavior information were collected from participants. Thereafter, participants received daily diabetes-eye related text messages for 12 weeks. Post-intervention, the impact of mHealth promotion on DR awareness and eye care behavior was assessed. Data was collected via sharing circles and surveys and underwent thematic and statistical analysis. Pre-intervention, participants indicated limited understanding of eye care costs/payment, guidelines, and eye complications and resolve to manage diabetes-eye conditions influenced eyecare. Also, fear originating from family history of diabetes, interaction with health care practitioners, and dependence on eye glasses affected their eye care. Participants requested information-resources on complications, prevention, and management of diabetes and DR which were included in the mHealth intervention. Age, diabetes status, and education level were significantly associated with DR knowledge, attitude, and practice scores. Post-intervention, the DR knowledge, attitude, and practice scores significantly improved. The DR attitude and practice post-score for individuals with diabetes increased compared to those at risk of diabetes. Women with higher education levels had higher pre-post-change in knowledge and practice score compared to women with low education levels. Older women had lower pre-post-change in practice score compared to younger women. Participants noted that voice or text messages via various mobile platforms, the telephone number used to send messages, the tone of messages, group activities, and message content were all important when using mHealth for health information. The mHealth intervention created awareness of DR and encouraged change in diabetes-eye care behavior. mHealth has the potential to be used for health education in different populations, and motivate, provide support, and empower individuals to prevent and manage chronic conditions and reduce the risk of complications.
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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 | 0.001 | 0.003 |
| 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".