Exploring Engagement of Patients with Diabetes via mHealth Applications: A Mixed-Methods Study with Primary Care Providers
Bibliographic record
Abstract
Research Aims \nThe use of mHealth diabetes self-management applications has emerged as a potential enabler for improving patient care. However, the uptake of such applications in vulnerable and marginalized patient populations remains unclear. This knowledge gap is concerning from a health equity perspective, especially when rates of diabetes are high among these populations. This study aims to examine how the understanding of health equity by primary care providers varies by practice type, as well as the barriers faced when attempting to use these applications. \n \nMethods \nEcological systems theory and Weber’s conflict theory informed the review of the literature and interpretation of the results. I collected the data from August to October 2020. A convenience sampling approach was used for the survey and I conducted the interviews either by phone or on an online platform. Participants received the survey link through listservs and newsletters from the collaborating organizations, such as The Alliance for Healthier Communities, the Association of Family Health Teams of Ontario, and the Nurse Practitioner Association of Ontario. Forty-eight participants completed the survey, and fifteen participants completed the interview. \n \nResults \nProviders in Community Health Centres had a slightly higher understanding and application of health equity in their practice when compared to non-CHC settings. The hierarchical regression analysis did not explain the variance beyond the number of years in practice, education/training on the social aspects of health, professional status, and service to minorities/vulnerable patients. Even though the results did not support the hypothesis, the overall model explained 33% of the variance in the main outcome. Thus, this study shows that the factors collectively impacting the engagement of providers with diabetic patients included funding, training, and location of the practice. As a result, we can conclude that these factors influenced their application of a health equity lens to their daily work and recommendations for mHealth diabetes application utilization.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".