Health Care Providers’ Emotional Responses to Their Patients’ Hypoglycemic Events: Qualitative Findings From the InHypo-DM Study, Canada
Bibliographic record
Abstract
OBJECTIVE: Hypoglycemia can cause psychological distress in people with diabetes; however, less is understood about the emotional impact of hypoglycemia on their health care providers (HCPs). This article focuses on the experiences and emotions of HCPs caring for patients with diabetes. METHODS: This was a descriptive qualitative study from the InHypo-DM research program. Purposive sampling was used to recruit 20 HCPs from a variety of professions for 30- to 45-minute semi-structured interviews. An iterative analysis was conducted to identify the overarching themes. RESULTS: Three overarching themes encompassed the responses of participants when their patients experienced hypoglycemia. The first was a sense of professional responsibility, as participants felt they must have failed or inadequately fulfilled their professional duties. The second was a more personal range of emotions such as sadness and guilt. The final theme was how these emotions created a "call to action," prompting participants to identify potential strategies to prevent future hypoglycemic events. CONCLUSION: This qualitative study highlights the emotional impact of patients' hypoglycemia on HCPs. Although it may have been expected that HCPs have a strong sense of professional responsibility, it was unexpected that these responses often became personal emotions. To ameliorate the negative impact of these responses on patient care, HCPs should engage in activities that enable them to anticipate and manage their own emotional responses. In addition, strategies to optimize hypoglycemia detection and prevention should be promoted.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".