Diabetes Educators’ Intended and Reported Use of Common Diabetes-Related Technologies
Bibliographic record
Abstract
BACKGROUND: Technology provides adjuvant and/or alternative approaches to care and may promote self-care, communication, and engagement with health care services. Common recent technologies for diabetes include continuous subcutaneous insulin infusions (insulin pumps), continuous glucose monitoring systems, smartphone and tablet applications, and telehealth (video conferencing). This study reports Australian diabetes educators' intentions and reported professional use of these technologies for people with type 1 diabetes, and factors predictive of this. METHODS: An anonymous, web-based questionnaire based on the technology acceptance model was distributed to members of the Australian Diabetes Educators Association through their electronic newsletter. Exploratory factor analysis revealed a 5-factor solution comprising confidence and competence, improving clinical practice, preparation (intentions and training), ease of use, and subjective norms. Logistic regression analyses identified factors predicting intention and use of technology. RESULTS: Respondents (n = 228) had high intentions to use technology. The majority reported using continuous subcutaneous insulin infusions, continuous glucose monitoring systems, and applications with patients, but usage was occasional. Confidence and competence independently predicted both intentions and use of all 4 technologies. Preparation (intentions and training) independently predicted use of each technology also. CONCLUSIONS: Discrepancies and dissonance appear between diabetes educators' intentions and behavior (intentions to use and reported technology use). Intentions were higher than current use, which was relatively low and not likely to provide significant support to people with type 1 diabetes for disease management, communication, and engagement with health care services. Continuing education and experiential learning may be key in supporting diabetes educators to align their intentions with their practice.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".