Digital Transformation of Faculty Development: Responding and Supporting Academia During Disruptions Caused by the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
INTRODUCTION: The coronavirus disease 2019 pandemic disrupted the current practices for teaching and learning in medical and health professions education, creating challenges and opportunities for rapid transition. The authors describe how McMaster University's Program for Faculty Development (MacPFD) responded to this disruption by engaging in a digital transformation. METHODS: The digital transformation process of MacPFD was mapped to the conceptual framework of digital transformation: Vial's building blocks of the framework. A new website was launched to host and disseminate the content. Subsequently, both the website and the content were promoted using social media tools. Content generation, Google Analytics, event registrations, and Zoom webinar attendance records were data sources for the results. Analysis of the data was based on the reach component of the RE-AIM framework. RESULTS: Six-month data range results were reported as producer-centered and user-centered outcomes. The former consisted of 54 resources from diverse content authors, whereas the latter received 33,045 page views from 26,031 unique users from 89 countries. Live webinar events had 1484 registrants, with 312 (21%) being guests from external institutions. Before the coronavirus disease 2019 disruption, MacPFD was a local program to support its faculty. DISCUSSION: The MacPFD's digital transformation shows a clear transition to a new "glocal" approach: an expanded global reach while still tending to our local development needs of the home institution's faculty members.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".