Bibliographic record
Abstract
BACKGROUND: Various technologies are commonly used to support type 1 diabetes management (continuous subcutaneous insulin infusion therapy, continuous glucose monitoring systems, smartphone and tablet applications, and video conferencing) and may foster self-care, communication, and engagement with health care services. Diabetes educators are key professional supporters of this patient group, and ideally positioned to promote and support technology use. The aim of this study was to examine diabetes educators' perceived experiences, supports, and barriers to use of common diabetes-related technologies for people with type 1 diabetes. METHODS: This qualitative ethnographic study recruited across metropolitan, regional and rural areas of Australia using purposive sampling of Australian Diabetes Educators Association members. Data were collected by semistructured telephone interviews and analyzed using thematic analysis. RESULTS: Participants (n = 31) overwhelmingly indicated that overall the use of technology in the care of patients with type 1 diabetes was burdensome for them. They identified 3 themes involving common diabetes-related technologies: access to technology, available support, and technological advances. Overall, these themes demonstrated that while care was usually well intentioned it was more often fragmented and inconsistent. Most often care was provided by a small number of diabetes educators who had technology expertise. CONCLUSIONS: To realize the potential benefits of these relatively new but common diabetes technologies, many diabetes educators need to attain and retain the skills required to deliver this essential component of care. Furthermore, policy and strategy review is required, with reconfiguration of services to better support care delivery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.229 | 0.057 |
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".