Implementation of a <scp>Blended‐Learning</scp> Perioperative Nursing Education Program in Canada
Bibliographic record
Abstract
Governmental COVID-19 mandates in Ontario, Canada, resulted in a backlog of perioperative procedures. Organization leaders were required to expand services after the pandemic; however, the ongoing nursing shortage and college-based structure of perioperative education programs complicated their response. In 2021, we developed an in-house perioperative education program using a blended-learning theory comprising online modules and videos, skills laboratory sessions, and clinical placement experiences. Nurses were required to apply for the program and remain employed at the facility for two years. Program evaluations showed that the novice nurses felt confident when beginning clinical experiences and preceptors believed the nurses were prepared for practice. Sixteen of 19 participants successfully completed the program, which helped resolve the staffing shortage. Novice nurses may benefit from a shadowing experience before applying for this type of program. Leaders in nonperioperative specialties should consider an in-house education program to help meet staffing needs in their areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".