Canadian Healthcare Providers’ Attitudes Towards Automated Insulin Delivery Systems
Bibliographic record
Abstract
Abstract Introduction We aimed to assess the current experience and attitudes towards Commercial and Do-it-yourself (DIY) automated insulin delivery (AID) systems among healthcare providers (HCP) across Canada. Methods A cross-sectional study was performed through electronic distribution of an anonymous survey to HCP licensed to practice in Canada looking after people with type 1 diabetes (T1D). Results Responses included 204 HCP across the multi-disciplinary team; dieticians (32.8%), nurses (31.9%), and endocrinologists (28.4%), looking after adults (51%) and children (23%) mainly in urban areas (85.7%). Respondents reported a median 100-500 patients with T1D per practice, with a median 6-24 current users/practice of Commercial compared to a median 1-5 current users/practice of DIY AID. The majority of HCP (72.7%) were comfortable supporting Commercial AID, whereas only 21.6% reported comfort supporting DIY AID use. A significant, although moderate correlation between HCP experience and comfort was seen; Commercial r=0.57(p<0.0001) and DIY r=0.45(p<0.0001). Respondents reported more barriers to DIY, relative to Commercial AID(p=0.001); unfamiliarity/lack of exposure and medico-legal risks were highlighted with DIY systems. Respondents suggested AID system education (both Commercial and DIY), for HCP and users, to improve HCP confidence. Conclusions Despite documented beneficial outcomes, AID systems are not widely used in the management of T1D in Canada. The need for both user and HCP education to improve familiarity with the systems, in addition to clarity in medico-legal guidance, have been identified as gaps, which if addressed, might enable the benefits of AID to be more widely available to people with T1D in Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".