Living With Hypoglycemia: An Exploration of Patients’ Emotions: Qualitative Findings From the InHypo-DM Study, Canada
Bibliographic record
Abstract
Hypoglycemia is one of the most common adverse events for people living with type 1 or type 2 diabetes. To gain a deeper understanding of patients' emotions regarding hypoglycemia, we conducted a descriptive qualitative study. Purposive sampling was used to recruit participants for a 30- to 45-minute semi-structured interview. The 16 participants included both women and men with either type 1 or type 2 diabetes, with a mean age of 53 years and mean time since diagnosis of 21 years. All participants had experienced more than one hypoglycemia event in the past year, ranging from nonsevere to severe. Data collection and analysis occurred in an iterative manner. Individual and team analyses of interviews were conducted to identify overarching themes and sub-themes. Thematic analysis revealed the unique interconnection among the emotions experienced by participants, including fear, anxiety, frustration, confidence, and hope. Time, experience, and reflection helped to build participants' confidence in their ability to manage a hypoglycemia event. Patients' emotions regarding hypoglycemia provide valuable insights into life with diabetes. Although hypoglycemia continues to evoke feelings of fear and anxiety, the role of hope may temper these emotions. Understanding the complex interplay of emotions concerning hypoglycemia can guide health care providers in improving clinical practice and promoting patient-centered interventions. Ultimately, health care providers can build patients' hypoglycemia-related confidence by using a strengths-based approach.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".