948-P: Patient Satisfaction with Virtual Diabetes Care during the COVID-Pandemic
Bibliographic record
Abstract
The COVID-pandemic has required changes to healthcare delivery and has been a stressful time for people with diabetes mellitus (DM) . Previous literature suggests that virtual health appointments for diabetes care can be effective and result in high patient satisfaction. However, it is unclear if patients with DM are satisfied with the widespread adoption of virtual care during the pandemic. The aim of this study was to evaluate the impact of virtual care during the pandemic on patient satisfaction in patients with type 1 and type 2 DM. The validated Patient Satisfaction Questionnaire (PSQ-III) was completed by 197 patients who had an in-person appointment in the six months before March 18, 2020 (pre-COVID) and a subsequent virtual appointment within six months after that date. For each form of healthcare delivery (i.e. in-person and virtual) , the satisfaction in six aspects of care was measured: general satisfaction, technical quality, interpersonal manner, communication, time spent with doctor, and accessibility of care. Both type 2 DM and age >55 years were associated with decreased general satisfaction with virtual care compared to in-person care. Both type 2 DM and female sex were associated with decreased technical quality of care. Accessibility of care decreased significantly in the overall study group. No significant differences in patient satisfaction were observed in the type 1 DM group. Despite our findings, 74% of respondents answered they would consider virtual health appointments in the future. Our data suggests that many patients welcome virtual health as an addition to their medical care. However, virtual health does not seem to deliver the same level of patient satisfaction as in-person appointments over the longer term. Future research on how fatigue with pandemic restrictions has affected patient satisfaction is needed to evaluate whether virtual health appointments are a viable option for long term diabetes care. Disclosure C.Chang: None. A.Dissanayake: None. M.Pawlowska: Advisory Panel; Novo Nordisk, Other Relationship; Medtronic. B.Schroeder: Advisory Panel; Novartis Canada, Novartis Canada, Novartis Canada, Novo Nordisk Canada Inc., Novo Nordisk Canada Inc., Novo Nordisk Canada Inc. J.Mackenzie-feder: Advisory Panel; Recordati S.p.A. A.White: Advisory Panel; AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".