Successful implementation of virtual care to overcome the challenges of managing gestational diabetes during the COVID-19 pandemic: a quality improvement project
Bibliographic record
Abstract
At the start of the COVID-19 pandemic, the Jim Pattison Diabetes and Pregnancy (JP DAP) clinic quickly switched from in-person to virtual care for patients with gestational diabetes (GDM) to reduce the risk of viral transmission. Poor glycaemic control in pregnancies increases the risk of maternal-fetal complications and thus women with GDM require education, frequent follow-up and treatment to reduce these risks. Delays in care could potentially result in increased maternal-fetal complications. We conducted a prospective, single-centre quality improvement (QI) study of women with GDM who attended the JP DAP clinic and delivered between 1 September 2019 and 31 March 2021. 2123 singleton pregnancies between 1 September 2019 and 31 March 2021 with GDM were analysed for this study. The time of referral to see the endocrinologist was lower than baseline in the first wave but rose significantly in the second wave. No-shows for appointments increased in the first wave but were lower than baseline after the implementation of time slots. There was no special cause variation for maternal-fetal complications pre pandemic, first wave or during the second wave. A patient satisfaction survey reported that 93% of respondents strongly agreed or agreed with the statement 'I was satisfied with the care provided to me over the telephone appointments'. The GDM education package, online educational videos in Hindi and English and the glucometer smartphone application helped to maintain the time of referral to first endocrinologist appointment in the first wave and therefore were considered an effective substitute for in-person education. Despite the delays in care seen in the second wave, there was no increase in maternal-fetal complications. Our clinic plans to continue using virtual tools for the foreseeable future.
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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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| 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".