Reflections From the Pandemic: Is Connectivism the Panacea for Clinicians?
Bibliographic record
Abstract
The COVID-19 pandemic and the recent increased interest in generative artificial intelligence (GenAI) highlight the need for interprofessional communities' collaboration to find solutions to complex problems. A personal narrative experience of one of the authors compels us to reflect on current approaches to learning and knowledge acquisition and use solutions to the challenges posed by GenAI through social learning contexts using connectivism. We recognize the need for constructivism and experiential learning for knowledge acquisition to establish foundational understanding. We explore how connectivist approaches can enhance traditional constructivist paradigms amid rapidly changing learning environments and online communities. Learning in connectivism includes interacting with experts from other disciplines and creating nodes of accurate and accessible information while distinguishing between misinformation and accurate facts. Autonomy, connectedness, diversity, and openness are foundational for learners to thrive in this learning environment. Learning in this environment is not just acquiring new knowledge as individuals but being connected to networks of knowledge, enabling health professionals to stay current and up-to-date. Existing online communities with accessible GenAI solutions allow for the application of connectivist principles for learning and knowledge acquisition.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".