Rapid Implementation and Evaluation of Virtual Health Training in a Subspecialty Hospital in British Columbia, in Response to the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: Adoption of virtual health (VH) solutions in healthcare has been challenging; this changed rapidly after implementation of physical distancing measures due to the COVID-19 pandemic. In response to the pandemic, British Columbia's Children's and Women's sub-specialty hospitals rapidly trained and scaled up support to equip staff and clinicians to use VH. Methods: Ninety-minute live online training workshops and frequently updated online support materials were offered for 6 weeks. Training was monitored via feedback collected at training sessions and a brief post-training survey. After training completion, a second survey was circulated to measure utilization outcomes and experiences with VH. Results: Eight hundred and ninety-five participants representing 82% of staff requiring support were trained through 101 sessions; 348 (38.9%) and 272 (30.4%) responses were collected for the monitoring and outcome surveys, respectively. Overall, 89% agreed that training was relevant to their needs; participants indicated average 58.1% (SD = 26.6) and 60.6% (SD = 25.2) increase in knowledge and confidence in VH after training; 90.1% had booked or conducted VH sessions. Increase in confidence was more pronounced in participants with lesser previous exposure to VH, but number of sessions conducted post-training and percentage of successful sessions were independent of previous exposure. For future training and support, participants suggested subject-tailored trainings, asynchronous trainings, and availability of experienced users. Discussion: Training is key to success of VH implementation. Moving forward, core competencies in VH should be developed to support standardization and allow for evaluation and quality improvement. Incorporation of VH training in continuous professional development and onboarding is also highly recommended.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".