COVID-19 Pivoted Virtual Skills Teaching Model: Project ECHO Ontario Skin and Wound Care Boot Camp
Bibliographic record
Abstract
OBJECTIVE: To describe a virtual, competency-based skin and wound care (SWC) skills training model. The ECHO (Extension for Community Healthcare Outcomes) Ontario SWC pivoted from an in-person boot camp to a virtual format because of the COVID-19 pandemic. METHODS: An outcome-based program evaluation was conducted. Participants first watched guided commentary and videos of experts performing in nine SWC multiskills videos, then practiced and video-recorded themselves performing those skills; these recordings were assessed by facilitators. Data were collected using pre-post surveys and rubric-based assessments. Descriptive statistics and thematic analysis were applied to data analysis. RESULTS: Fifty-five healthcare professionals participated in the virtual boot camp, measured by the submission of at least one video. A total of 216 videos were submitted and 215 assessment rubrics were completed. Twenty-nine participants completed the pre-boot camp survey (53% response rate) and 26 responded to the post-boot camp survey (47% response rate). The strengths of the boot camp included the applicability of virtual learning to clinical settings, boot camp supplies, tool kits, and teaching strategies. The analysis of survey responses indicated that average proficiency scores were greater than 80% for three videos, 50% to 70% for three of the videos, and less than 50% for three of the videos. Participants received lower scores in local wound care and hand washing points of contact. The barriers of the boot camp included technical issues, time, level of knowledge required at times, and lack of equipment and access to interprofessional teams. CONCLUSIONS: This virtual ECHO SWC model expanded access to practical skills acquisition. The professional development model presented here is generalizable to other healthcare domains.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".