Health Need, Demand, and Cost Burden of Type 1 Diabetes Health Technologies on Families
Bibliographic record
Abstract
Insulin pump therapy and continuous glucose monitoring (CGM) can benefit children living with type 1 diabetes (T1D) and their families. Families can accrue costs for these, despite public and private funding sources. The impact of a government program in the province of Ontario, the Assistive Devices Program (ADP) for insulin pump therapy, is not well understood in the context of the paediatric population’s T1D health needs, family’s cost burden or diabetes nurse educator’s (DNE) role. The first aim of the study was to better understand the paediatric population’s type 1 diabetes health needs and families’ cost burden in the context of the ADP. The second was to expand understanding of the diabetes nurse educator’s role and impact in the context of the ADP, and helping families’ access T1D health technologies. An explanatory sequential mixed methods design comprised of a cross-sectional, web-based survey of parents and semi-structured interviews with DNEs were conducted. Survey data analysis involved descriptive statistics, and a cost analysis of parents direct and indirect costs including sensitivity analyses on select outcomes. An inductive content analysis of qualitative survey and interview data was conducted, before interpreting the study results. Annual expenditures for insulin pump therapy generally exceeded the $2,400/year ADP grant. Parents of a child requiring a multiple daily injection regime accrued slightly more expenditures on average over 12 months ($3,605.25) compared to insulin pump therapy ($3,447.72), when accounting for both direct and indirect costs as well as repayments by government programs and third-party payers. Parents and DNEs perceived benefits and barriers (e.g., cost) of pump therapy and CGM. Diabetes nurse educators helped families access the ADP, and worked with them and other diabetes team members to ensure children maintained program eligibility. Study results could help influence the Ontario government’s decision-making related to future health technology funding for T1D management and diabetes programs. Findings also provide greater clarity about the DNE’s role in the context of government programs such as the ADP, and highlight recommendations for nursing scope of practice, and broader health policy.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".