Analyzing Diabetes Care Providers’ Perspectives on Type 2 Diabetes Management Among Immigrant Women in Hamilton, Ontario: A Qualitative Study
Bibliographic record
Abstract
Purpose: To investigate the factors influencing diabetes management from the perspectives of Diabetes Care Providers (DCPs) practicing in Hamilton, Ontario, focusing on the relationships they have established with their patients/clients who are immigrant women. Method: Using a qualitative content analysis approach, interviews were conducted with six DCPs who practiced in Hamilton, ON. Respondents were affiliated with either the Boris Clinic, St. Joseph's Healthcare Hamilton, or the Hamilton Family Health Team. Results: Factors influencing diabetes self management for immigrant women as perceived by DCPs encompassed aspects related to DCPs, the immigrant women, and the healthcare system. From the insights of the DCPs, factors that impeded them from delivering effective DSM education included challenges in patient-provider communication, issues of cultural and ethnic concordance, the significance of trust in the provider, and language barriers. The factors influencing immigrant women's participation in DSM based on the insights of DCPs encompassed knowledge and awareness, language barriers, gender roles, socioeconomic considerations, acculturation, and social isolation. Factors influencing the healthcare system in providing DSM education according to DCPs involved the availability of interpretation services, deficiencies in training, a lack of diversity in clinical research and diabetes care teams, and inadequate promotion of DSM awareness. Conclusion: Drawing from the findings, it is advisable to enhance diversity within diabetes care teams, emphasizing the inclusion of professionals from various disciplines. Increasing cultural competency in DSM guidelines and raising awareness to diabetes screening can help decrease the prevalence of Type 2 Diabetes among immigrant women. Future studies focusing on the perspectives of immigrant women residing in Hamilton, ON, can offer deeper insights into this health concern.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".