An expanded model of diabetes care based in an analysis and critique of current approaches
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine and critique various models guiding the care and education of people with diabetes, to develop more helpful and effective approaches to care. The focus is on relationships and communication between patients and healthcare providers. BACKGROUND: Many patients are not adhering to the recommended treatments, hence it seems that effective diabetes care is difficult to achieve, particularly for patients of lower socio-economic status, who are disproportionately afflicted. The results are usually devastating, and lead to serious health complications that incisively diminish quality of life for patients with diabetes, frustrate healthcare providers and increase healthcare costs. DESIGN: Critical review. METHOD: This paper represents a critical review of various approaches to diabetes care and education. A CINAHL search with relevant key words was carried out and selected exemplary research studies and articles describing and/or evaluating the various approaches to diabetes care and management were examined. Particular attention was paid to how the paradigmatic underpinnings of these approaches construct patient - healthcare provider relationships. CONCLUSION: The literature revealed that the traditional top-down approaches to care were largely ineffective, while collaborative approaches, based in respect and taking the whole persons and their unique situations into account, were found to be central to good care. Further, an integration of the different kinds of knowledge contained in the various approaches can complement and extend one another. RELEVANCE TO CLINICAL PRACTICE: Avoiding devastating complications by improving the management of diabetes and overall quality of life of patients is a worthwhile goal. Therefore expanding diabetes care beyond the traditional bio-medical model to develop more effective approaches to care is of interest to all healthcare professionals working in this area.
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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 | 0.054 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".