Bibliographic record
Abstract
Virtual education transcends time zones, geographic boundaries, and distances. With virtual education, “students study a digital curriculum taught by instructors that lecture online via video or audio. This instruction can take place either in a self-paced (asynchronous) environment or in a real-time (synchronous) environment.”1 Virtual education is not new; for example, Excelsior University has been a pioneer in this type of learning since 1971. In this issue of Advances, two articles report results of virtual education of interest to providers in our specialty. Özkaya and Harputlu, based in Turkey, present research on video education provided at home to persons with newly created stomas. In comparison with patients who did not receive video education, patients in the video education group had higher scores on the Stoma Self-Efficacy Scale and the Ostomy Adjustment Inventory-23 and could better care for their stoma. An article by Canadian authors Sibbald et al reports their experience with skin and wound care virtual skills education. There are three key elements to this revolutionized education: Project ECHO (Extension for Community Healthcare Outcomes), patient navigation, and virtual skills education. Project ECHO Skin and Wound is designed to build interprofessional skin and wound care teams across Ontario by moving knowledge, not patients, to spokes that include indigenous, remote, isolated, and northern communities. Each interprofessional accredited session includes an interactive didactic component, a clinician corner, and two de-identified cases from the spokes that follow the ten recommendations from Wound Bed Preparation 2021 (WBP-2021).2 The second component is patient navigation.3 In the Canadian healthcare system, 20% of wound care clients have complex wounds that consume 80% of the total system cost. Patient navigation is most often used in cancer care; however, patients with chronic wounds also often experience difficulty using the healthcare system, arranging doctors’ appointments, and scheduling diagnostic testing. In the published model, clients who are on homecare for long periods of time or have high resource utilization are documented by wound care specialist nurses using the WBP-2021 model. The WBP-2021 assessment includes use of an audible handheld Doppler measurements with an infrared thermometer, and photographic wound documentation. A wound care specialist conducted a scheduled 60-minute assessment and determined provisional diagnosis, ordered diagnostic testing and specialist consultations, and developed a comprehensive treatment plan. The study authors report that 29% of patients healed; 67% of wounds decreased in size; and over 70% of patients had fewer nursing visits, less supply usage, better infection management, and reduced levels of pain. The third component of the triad of innovations occurred when an in-person boot camp was cancelled due to COVID-19 lockdown. The Ontario government sponsor approved virtual videos based on a skin and wound care curriculum that incorporated three critical steps: Defined skills with interprofessional teams from ECHO and the International Interprofessional Wound Care Course faculty Translated competency-based skin and wound care skills through nine interactive videos Developed qualitative and quantitative assessments with pre/post content evaluations by two independent PhD assessors. The virtual format was accredited for 10 hours through Queens University. Participants (85% nurses) attended two 4-hour sessions that introduced the video content and included interactive quizzes (eg, play audible handheld Doppler audio tapes to identify Doppler sounds). Other assignments included recording videos on hand hygiene, simplified 60-second screen for the high-risk diabetic foot, and compression bandaging. Most participants (87%) reported changing practice after the sessions, including improved patient assessments, using the audible handheld Doppler, improved compression bandaging, enhanced patient-centered care, and an increased interprofessional network. Virtual boot camps provide opportunities for egalitarian professional development to reach more participants while also offering more equitable access by addressing geographic barriers to learning. Are you ready for change and the use of virtual education to enhance health care professional practice and improve patient care?R. Gary Sibbald, MD, Med, FRCPC (Med Derm), FAAD, MAPWCA, JMElizabeth A. Ayello, PhD, MS, RN, CWON, MAPWCA, FAAN
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".