A cross‐cultural comparison of the developmental evolution of expertise in diabetes self‐management
Bibliographic record
Abstract
AIMS: The authors compare the findings of two research studies, one conducted in Japan and the other in Canada, about the developmental evolution of self-management of diabetes. In this article, the authors identify the similarities and differences that exist in the research data, proposing that the differences are situated in the different cultural perspectives of self-management that exist in both countries. BACKGROUND: Researchers have acknowledged that self-management has cultural dimensions. Despite this, however, there are few studies that have provided a cross-cultural comparison of the experience of self-management among different cultural groups. DESIGN: The authors conducted a critical comparative analysis of two models of developing expertise in diabetes self-management. The review included an analysis of the cultural meanings of the various terms and the underlying assumptions of both models. CONCLUSIONS: The models shared many similarities; however, their differences were identified, such as the meaning and interpretation of various words or experiences, and shaped by the culturally bound perspectives of self and health. RELEVANCE TO CLINICAL PRACTICE: The findings serve as a caution to imposing ethnocentric views and interpretations in diabetes care. In addition, they remind us about the importance of asking people with diabetes about what they understand, desire and understand. The findings challenge nurses to reflect on how the development of self-management of diabetes in various national contexts is influenced by health care practices that focus on control or harmony.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".