Bibliographic record
Abstract
Several intriguing questions were raised during my presentation on Mental readiness and pediatric neurosurgery at the Brazil Society for Pediatric Neurosurgery (BSPN) Scientific Meeting–Webinar on April 15, 2020. Does arrogance disturb mental readiness techniques? Can mindfulness techniques improve surgical readiness? Can simulation models improve mindfulness? How do you stay focused on routine procedures you’ve done 800 times? The University of Ottawa (Canada)—where I am a research scientist in at the McLaughlin Centre for Population Health Risk Assessment in the Faculty of Medicine—has conducted extensive studies on a new discipline called operational readiness. It correlates research with the performances of Olympic athletes and people in high-risk occupations, like surgeons, air traffic controllers and police, whose standards of excellence have life-and-death consequences. So, what can we learn from the best in these fields? Plenty. Our ongoing work makes it clear that effective focusing practices impact one’s performance. But, it is not always easy to “be there for the task-at-hand with all your attention on the right things for the duration of the event without distracting thoughts.” Here are five focusing techniques used by elite surgeons and other peak performers compiled by researchers at the University of Ottawa.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".