Exploring patients’ perceptions for insulin therapy in type 2 diabetes: a Brazilian and Canadian qualitative study
Bibliographic record
Abstract
Camila Guimarães2, Carlo A Marra1, Sabrina Gill1, Graydon Meneilly1, Scot Simpson3, Ana LPC Godoy2, Maria Cristina Foss de Freitas2, Regina HC Queiroz2, Larry Lynd11The University of British Columbia, Canada; 2University of São Paulo, Brazil; 3The University of Alberta, CanadaObjective: To explore which attributes of insulin therapy drive patients’ preferences for management in Canada and Brazil.Methods: A qualitative design was implemented in which a total of 32 patients with type 2 diabetes from Canada and Brazil, were interviewed in one of the 4 focus groups, or 16 individual interviews. Eighteen participants (56%) were women and fourteen participants (44%) were men (15 insulin nonusers and 17 insulin users). Two focus groups of 4 participants each and 9 individual interviews were conducted in Brazil. In Canada, 2 focus groups of 4 participants each and 7 individual interviews were conducted. A framework analysis was used to analyse all data.Results: Brazilian participants, when considering two insulin treatments, would prefer the one that had fewer side-effects (specially hypoglycemia events), was noninjectable, had the lowest cost and was most effective. Meanwhile, Canadian participants would prefer a treatment that had fewer side-effects (specially weight gain), was less invasive, was more convenient and was most effective.Conclusions: Finding the insulin-delivery system and the attributes of insulin therapy that best meet patients’ preferences may lead to improved control, through improved compliance, which may ultimately reduce the financial burden of the disease and improve quality of life.Keywords: type 2 diabetes, insulin administration, glycemic control, weight gain, hypoglycemia, qualitative study, patients’ preferences
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".