CAEP 2015 Academic Symposium: Current State and Recommendations to Achieve Adequate and Sustainable Funding for Emergency Medicine Academic Units
Bibliographic record
Abstract
OBJECTIVES: To describe the current state of academic emergency medicine (EM) funding in Canada and develop recommendations to grow and establish sustainable funding. METHODS: A panel of eight leaders from different EM academic units was assembled. Using mixed methods (including a literature review, sharing of professional experiences, a survey of current EM academic heads, and data previously collected from an environmental scan), 10 recommendations were drafted and presented at an academic symposium. Attendee feedback was incorporated, and the second set of draft recommendations was further distributed to the Canadian Association Emergency Physicians (CAEP) Academic Section for additional comments before being finalized. RESULTS: Recommendations were developed around the funding challenges identified and solutions developed by academic EM university-based units across Canada. A strategic plan was seen as integral to achieving strong funding of an EM unit, especially when it aligned with departmental and institutional priorities. A business plan, although occasionally overlooked, was deemed an important component for planning and sustaining the academic mission. A number of recommendations surrounding philanthropy consisted of creating partnerships with existing foundations and engaging multiple stakeholders and communities. Synergy between academic and clinical EM departments was also viewed as an opportunity to ensure integration of common missions. Education and networking for current and future leaders were also viewed as invaluable to ensure that opportunities are optimized through strong leadership development and shared experiences to further the EM academic missions across the country. CONCLUSIONS: These recommendations were designed to improve the financial circumstances for many Canadian EM units. There is a considerable wealth of resources that can contribute to financial stability for an academic unit, and an annual networking meeting and continuing education on these issues will facilitate more rapid implementation of these recommendations.
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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".