Qualitative study of employment of physician assistants by physicians: benefits and barriers in the Ontario health care system.
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences and perceptions of Ontario physician assistant (PA) employers about the barriers to and benefits of hiring PAs. DESIGN: A qualitative design using semistructured interviews. SETTING: Rural and urban eastern and southwestern Ontario. PARTICIPANTS: Seven family physicians and 7 other specialists. METHODS: The 14 physicians participated in semistructured interviews, which were audiorecorded and transcribed verbatim. An iterative approach using immersion and crystallization was employed for analysis. MAIN FINDINGS: Physician-specific benefits to hiring PAs included increased flexibility, the opportunity to expand practice, the ability to focus more time on complex patients, overall reduction in work hours and stress, and an opportunity for professional fellowship. Physicians who hired PAs without government financial support said PAs were affordable as long as they were able to retain them. Barriers to hiring PAs included uncertainty about funding, the initial need for intensive supervision and training, and a lack of clarity around delegation of acts. CONCLUSION: Physicians are motivated to hire PAs to help deal with long wait times and long hours, but few are expecting to increase their income by taking on PAs. Governments, medical colleges, educators, and regulators must address the perceived barriers to PA hiring in order to expand and optimize this profession.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".