The impact of prior professional degrees and experiences on Manitoba Master of Physician Assistant Studies graduates didactic year, clinical year, and first job in relation to CanMEDS-PA: A quality improvement survey response study
Bibliographic record
Abstract
Background: The Manitoba Master of Physician Assistant Studies (MPAS) program attracts applicants with a variety of different pre-professional degrees and experiences. Currently there is very little research with respect to whether certain pre-professional degrees or experiences have an impact of PA students’ overall success throughout the program. Purpose: The purpose of this study is to determine if Manitoba MPAS graduates feel that their pre-professional degrees, healthcare/clinical experiences, and non-healthcare/clinical experiences had an impact on their success in their didactic year, clinical year, and first job as a PA in relation to the CanMEDs-PA competencies. Methods: This quality improvement cross-sectional survey response study used an online survey sent to Manitoba MPAS graduates to obtain quantitative data and emergent themes related to the perceived impact of pre-professional degrees, healthcare/clinical experience and non-healthcare/clinical experience on student success. Results: Forty-seven survey responses met the inclusion criteria to be included in the study. Pre-professional degrees were felt to be most impactful during didactic year, with 76.59% of respondents reporting this. Of those who had healthcare/clinical experience, 80.00% felt this to have an impact during their didactic year, clinical year and first job as a PA. Non-healthcare/clinical experience were felt to be most impactful on clinical year, as noted by 65.71% of respondents who had this experience. Conclusion: Manitoba MPAS graduates felt their pre-professional degrees and experiences had an impact on their success in their didactic year, clinical year and, to a lesser extent, their first job as a PA. Specific degrees or experiences over others were not noted to be particularly influential in aiding in this success.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".