Practice meta-environment of the cardiovascular perfusionist
Bibliographic record
Abstract
Though historically the development of cardiovascular perfusion grew out of the need for cardiopulmonary bypass, the application of technologies of extracorporeal support has more recently expanded beyond the traditional domain of the cardiac surgical operative and peri-operative environment. As a result, perfusionists are sometimes required to work in novel clinical settings. As part of our recent national survey to evaluate the effects of changes in entry-to-practice criteria introduced in Canada in 2006, we asked perfusionists if their current position as a perfusionist involves work outside the OR. We found that, in addition to regularly working in the Intensive Care Unit and Cardiac Catheterization Lab, 55.3% of respondents reported working "occasionally" in the Emergency Room and 74.7% reported working "occasionally" or "often" in other clinical areas. However, while 96% of respondents believed their training adequately prepared them for their job as a perfusionist, only 68% felt their training adequately prepared them for their duties outside the operating room. We also noted a trend that admission under experience-based entry-to-practice criteria was associated with a higher likelihood of perceived adequacy of training in preparation for duties outside the OR than education-based admission criteria (72% vs 59.4%, p=0.065). These findings raise important questions pertaining to the sufficiency of perfusion education in Canada and the influence of soft skills in preparing perfusionists for their duties, and indicate that a systematic study of the practice environment of cardiovascular perfusionists is timely.
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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 | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".