Supporting patients with type 1 diabetes using continuous subcutaneous insulin infusion therapy: Difficulties, disconnections, and disarray
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Use of continuous subcutaneous insulin infusion therapy in type 1 diabetes management is high. However, the incorporation of this technology into self-care is not without challenges, and the support of an appropriately skilled health care team is recommended. This study aimed to examine the support context for patients using continuous subcutaneous insulin infusion therapy from the health care professional perspective, as well as contextual influences for health care professionals and their patients. METHODS: This ethnographic qualitative study was undertaken in New South Wales, Australia. Recruitment occurred using a snowball sampling technique, beginning with members of an established diabetes service group. Data were collected through the use of semistructured interviews undertaken by telephone and analysed using thematic analysis. RESULTS: Data were obtained from 26 interviews with staff from diverse professional backgrounds. An overarching theme of difficulties, disconnections, and disarray emerged, with findings indicating that participants perceived difficulties in relation to shortages of health care professional continuous subcutaneous insulin infusion-related expertise, and disconnected and disarrayed service structures and process, with barriers to access to these devices. Individual health care professionals were left to manage somehow or opted not to engage with related care. CONCLUSIONS: Findings provide insights from health care professionals' perspectives into the complexity of providing support for patients using continuous subcutaneous insulin infusion therapy across diverse contexts, and provide a platform for further research and service development. The need for consistent and coordinated care, and the infrastructure to facilitate this, flags an opportunity to drive integration of care and teamworking across as well as within settings and disciplines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".