An Exploration of the Barriers to Diabetes Management Among West African Immigrants in the United States
Bibliographic record
Abstract
Purpose: To explore the barriers to diabetes management among West African immigrants living in Rhode Island. Study Design and Methods: Semi-structured interviews with adults (N=5); men (n=2), women (n=3), Nigerian (n=3), Liberian (n=1), and Ghanaian (n=1) ages ≥18 with Type 1 or Type 2 diabetes, living in Rhode Island. Demographic information of the participants was obtained using a questionnaire. Interviews were conducted by the researcher and lasted approximately 50 minutes, and were scheduled to accommodate participants’ needs. Interviews were audio-taped and statements of the study participants were recorded. Subjects were redirected to clarify responses when necessary. Content analysis and coding, as proposed by Miles and Huberman (1994), were completed. Eight pertinent themes were identified. Results: All participants had Type 2 diabetes and reported various barriers to diabetes management: (1) financial difficulties, (2) poor dietary habits, (3) non adherence to daily maintenance, (4) cultural attachment to traditional management of diabetes, (5) cultural beliefs, (6) negative relationship with primary care doctor, (7) non-adherence to medication regimen, and (8) their practitioner’s inadequate knowledge of cultural care. Clinical Implications: These findings revealed that barriers exist for suitable diabetes management by some West African immigrants living in RI. Reducing the risk for complications, morbidity, and mortality can only occur with reduction of identified barriers.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".