385-P: Health-Care Providers’ Emotional Responses to Their Patients’ Hypoglycemic Events: The InHypo-DM Study
Bibliographic record
Abstract
Minimizing hypoglycemia is a key clinical goal for healthcare providers (HCPs) in the care of patients with T1DM and T2DM as the associated morbidity and mortality of hypoglycemia is well understood. Yet, there is a paucity of literature regarding the impact of reported hypoglycemia on HCPs. Using qualitative data from the InHypo-DM Study, Canada’s largest mixed methods study of hypoglycemia to date, we explored the emotional impact of patients’ hypoglycemia on HCPs. Our research team conducted semi-structured interviews lasting 30-45 minutes. Interviews were transcribed verbatim. An iterative and interpretative process was used during both individual and team analysis to identify the main themes. A purposive sample of twenty HCPs was interviewed including endocrinologists (4), family physicians (4), nurse practitioners (3), and diabetes educators (9). Three themes emerged in the analysis: (1) a sense of professional responsibility with the subsequent feeling that the HCP must have failed or inadequately fulfilled their professional duties; (2) a mix of personal emotions including sadness, “feeling bad,” guilt and distress; and (3) the desire to act on these emotions through a “call to action” expressed as a strong desire to identify potential causes of patients’ hypoglycemia and ways in which to help prevent future events. This qualitative study highlights the emotional impact on HCPs as they care for patients at risk of hypoglycemia. While it may have been expected that HCPs have a strong sense of professional responsibility, it was unexpected to discover that these responses often became personal emotions. Clinical strategies that support HCPs in their efforts to optimize hypoglycemia detection and prevention should be promoted. Such strategies could help facilitate not only reductions in hypoglycemia among patients, but also lessen the emotional burden experienced by diabetes care providers over the span of their careers. Disclosure S.M. Reichert: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Novo Nordisk Inc., Sanofi, Servier. Consultant; Self; Abbott, AstraZeneca, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Canadian Institutes of Health Research. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Novo Nordisk Inc., Sanofi. J.B. Brown: None. Y. Valliere: None. C. McLachlan: None. S. Webster-Bogaert: None. A. Ratzki-Leewing: Consultant; Self; Eli Lilly and Company, Novo Nordisk A/S. Research Support; Self; Sanofi. B.L. Ryan: None. N.H. Au: None. S.B. Harris: Advisory Panel; Self; Abbott, AstraZeneca, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Abbott, AstraZeneca, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; AstraZeneca, Canadian Institutes of Health Research, Eli Lilly and Company, Health Canada/First Nations and Inuit Health Branch, Janssen Pharmaceuticals, Inc., JDRF, Lawson Foundation, Novo Nordisk Inc., Sanofi. Other Relationship; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk Inc., Sanofi. Funding Sanofi Canada
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".