The Time-out: Team Involvement – Patient, Practitioner, Program and Pathways to Greener ORs (in Alberta)
Bibliographic record
Abstract
In this engaging and informative presentation, Dr. Tara Klassen discusses the implementation of surgical innovations within Alberta's healthcare system. With a background in Physiology, Cell, and Developmental Biology, she leads efforts to transform perioperative care across the province by balancing evidence-based practices and innovative technologies. Dr. Klassen emphasizes the importance of collaboration among diverse stakeholders including surgeons, program managers, and procurement teams to drive positive change in the healthcare landscape. She highlights the challenges and motivations behind green initiatives aimed at reducing environmental impact and improving sustainability in surgical settings. By detailing specific case studies and practices being employed in Alberta, including waste diversion efforts and the integration of recycling programs, Dr. Klassen provides insights into how healthcare professionals can effectively contribute to a greener future. Her presentation encourages attendees to engage actively in discussions and collaborations to further the mission of health system improvements, blending patient care with environmental responsibility.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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".