Imagining the Future of Learning in Healthcare: The GAME 2019 #FuturistForum
Bibliographic record
Abstract
The GAME 2019 #FuturistForum involved an exchange of ideas and perspectives on the future of learning in healthcare and necessary evolutions to sustain future health systems. This event allowed for reflection and discourse around a) what medical learning or learning in healthcare may look like 10-15 years from now, b) how technology would impact that evolution, and c) what collaborative roles distinct stakeholders would play. Seventy-five (75) key stakeholders, experts from various fields, participated in the two-day event. Four multifaceted themes were uncovered from the qualitative analysis, which are: learning will be lifelong and outcome-based, the health system will follow a preventive care model, technology will be an enabler of evolution in education and health systems, and that multi-level collaboration will support and sustain future progress. Future implications, exacerbated by the ongoing COVID-19 pandemic, and study limitations are described.
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 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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".