Using High Fidelity Simulation to Enhance Perceptions of Competency and Safe Practice in Anesthesia Assistants.
Bibliographic record
Abstract
BACKGROUND: The Michener Institute for Applied Health Sciences educates health professionals and addresses human resources requirements in healthcare in Ontario. In 2005 a new program for Anesthesia Assistants (AAs) was designed and implemented to address waiting times for surgical procedures. An AA under the supervision of an Anesthesiologist, can assist in the provision of anesthetic care, thereby potentially facilitating the flow of patients through operating rooms and reducing waiting times for procedures. High fidelity simulation was integrated throughout a 13 week didactic curriculum to aid in achieving competency in newly identified skill areas for AAs. A clinical education component followed the didactic phase of the program. The purpose of the study was to examine the perceptions of AA students as they pertained to enhancing competency and safe practice. METHODS: Following ethics approval, qualitative methodology was used to elicit the perceptions of the learners (n=24) in each of the first 2 cohorts of the Anesthesia Assistant program. Learners consisted of Registered Respiratory Therapists, all of whom had precious experience working in the Operating Room. Learners were asked to keep a reflective journal of their experience with learning via high fidelity simulation. Thematic analysis of was conducted of the learners’ reflective journals. In addition, a focus group was conducted following completion of the classroom/simulation portion of the curriculum. RESULTS: There were four major themes identified throughout the data: anxiety, comfort, patient safety and added value. Participants expressed initial anxiety with using simulation and a strong fear of being team leader. By week two, they began expressing their perceptions of the impact this method of training was having on their skill level and competency. Gaps in knowledge and weaknesses in skill demonstration were highlighted through simulation. Debriefing activities provided opportunities for formative feedback. By week six, most participants expressed a positive perception of their competency. They saw value in simulation use in the integration of skills and behaviours in a safe environment. They expressed gratitude that they were able to make mistakes during their learning process without endangering patients. By week six participants no longer expressed fear of assuming the role of team leader. While comfort with this role was widely variable over the first six weeks of the program, many participants expressed the desire to assume the role more frequently. Satisfaction with simulation as a method of teaching and evaluating AA competency based skills was strongly expressed in reflections and the focus group. CONCLUSIONS: Simulation activities integrated into the curriculum of an AA program were perceived as effective in enhancing skills and competency while maintaining patient safety. Participants were cognizant of the value of simulation with respect to both practice of new skills and assessment of competency. Although initially anxious about assuming the role of team leader, the simulation enhanced curriculum allowed opportunities to safely practice which resulted in increased comfort with this role. Further study could be done to explore the correlation between enhanced perception of competency and actual competency pre and post simulated learning experiences.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".