Effects of a change in entry-to-practice criteria for cardiovascular perfusion in Canada: results of a national survey
Bibliographic record
Abstract
INTRODUCTION: Years of experience and level of education are two important determinants of a clinician's expertise. While entry-to-practice criteria for admission to perfusion training in Canada changed from clinical experience-based criteria to education-based criteria in 2006, the effects of these changes have not been studied. OBJECTIVE: To determine the academic and clinical backgrounds of perfusionists in Canada, ascertain perceptions about the adequacy of training and evaluate the effects of the changes on the composition of the perfusion community of Canada. METHODS: An electronic questionnaire was distributed to all practicing perfusionists in Canada, addressing details regarding clinical experience, academic education and perceptions about the adequacy of training. RESULTS: Two hundred and twenty-eight questionnaires were completed, representing a 72% response rate. Perfusionists admitted under academic-based criteria have significantly higher levels of education (100% degree holders vs 69.1%, p<0.001), but less antecedent clinical training and experience (median, IQR: 0, 0 - 4.5 years vs 2, 2 - 8 years, p<0.0001), are younger (median age range 31-35 years vs 51-55 years, p<0.0001), more likely to be female (58.7% vs 41.3%, p=0.006) and are significantly more likely to enter perfusion because of attraction to the type of work (p=0.045). Many perfusionists (70, 32%) in Canada believe themselves inadequately trained for their clinical assignments outside the OR. In addition, 19% of perfusionists plan to retire over the next 10 years. CONCLUSIONS: The introduction of education-based entry criteria has changed the academic and clinical experience levels of perfusionists in Canada. Strategies designed to better prepare perfusionists for their clinical assignments outside the OR are merited.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".