Financial challenges faced by Physician Assistant students when seeking financial assistance to attend PA schools in Canada - A Survey based approach
Bibliographic record
Abstract
Introduction: Physician Assistant (PA) students find it challenging to obtain aid and/or loans to fund their PA school tuition costs in addition to their living expenses in Canada especially through private institutions. At present, there is no research as to why obtaining loans is challenging for PA students in Canada. The main goal of this study was to identify what reasons financial institutions proclaim when refusing a loan application to fund PA schools in Canada. Methods: We used a survey-based approach to gather our data. Online surveys were distributed to all three PA schools in Canada to obtain a higher number of responses. Results: Our results show that 39.6% of the participants stated that they worried about financial burden in PA school fairly often while 30.2% stated that they worried very often. 33.9% of the participants stated that their mental health was fair during PA school while 9.4% of the participants stated that their mental health was poor during PA school. 48.6% of the participants stated that it was extremely difficult to obtain private loans from financial institutions. Conclusion: This study showed that obtaining funding for PA schools from financial institutions in Canada is challenging as we expected. The survey respondents provided various reasons for why this is the case which were explored throughout the paper. Students were also stressed in PA school and most participants rated their mental health as poor or fair in our study.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".