Perceptions of a Culturally Tailored Adapted Program to Prevent Type 2 Diabetes
Bibliographic record
Abstract
Objective: To explore African and Caribbean immigrants’ perceptions of a culturally tailored intervention to prevent type 2 diabetes. Methods: Using a purposive sampling scheme, 29 participants (26 women and 3 men, mean age 48 years and average length of residency of 22 years in Canada) participated in this study. The researchers used in-depth focus groups and individual surveys to collect qualitative data from participants. Following these interviews, participants individually reviewed the intervention and provided written feedback on their perceptions of the intervention. Data were analyzed using thematic analysis. Findings: Three themes emerged from the data: motivating factors to engage in physical activity and healthy eating, barriers that prohibit engaging in physical activity and increasing knowledge about food selection and modification of diet. In addition to the themes, participants had positive perceptions of the intervention. Eighty six percent rated the intervention as very useful and 83% were very satisfied with its contents. They reported that the intervention was culturally consistent with their beliefs, values and cultural practices in preventing type 2 diabetes. Conclusion: Perceptions of this culturally tailored intervention provided valuable evidence to assist the researchers in moving forward to the next level of research development such as evaluating the effectiveness of the intervention on the targeted groups’ outcomes: self-care knowledge, performance of physical activity, healthy eating practices and self-efficacy.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".