Evaluation of Diabetes Hotline Service Implemented During the COVID-19 Pandemic: A Dynamic Adaptation
Bibliographic record
Abstract
Background:The coronavirus disease 19 (COVID-19) pandemic presented major challenges for people living with diabetes. People with diabetes were identified as being at increased risk of serious illness from COVID-19. The lockdown and preventive measures, including social distancing measures, implemented worldwide to limit the spread of COVID-19 had negatively impacted access to diabetes care, including self-management services, challenging the way modern medicine had been practiced for decades. This article aims to shed light on the implementation and evaluation of the Diabetes hotline service run by trained diabetes patient educators during the pandemic in Qatar. Methods:The logic model is utilized to showcase the implemented strategies/activities and the output monitoring process. An online survey among hotline users was undertaken to gather feedback on patients' overall experience of using the service and physician feedback. Results:Of the 464 patients surveyed, over 92% stated that they would recommend the hotline service to others, and over 90% indicated that they considered the hotline a trusted and reliable resource for diabetes education and advice. Conclusion:It is expected that the lessons learned from maintaining health care delivery services during the COVID-19 pandemic have created new ways of providing standard care and meeting the needs of people with diabetes. Future research should study the clinical outcomes for patients who benefited from the hotline services and the impact on the well-being of people with diabetes.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".