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Record W4378373981 · doi:10.1177/08404704231173612

Physician ratings of physician assistant competencies and their experiences and satisfaction working with physician assistants: Results from the supervising physician survey in Ontario, Canada

2023· article· en· W4378373981 on OpenAlexaffabout
Kristen Burrows, Leslie Nickell, Paul Krueger

Bibliographic record

VenueHealthcare Management Forum · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMentorshipPhysician assistantsEconomic shortageHealth careNursingFamily medicineMedicineJob satisfactionMedical educationPsychologyNurse practitionersPolitical science

Abstract

fetched live from OpenAlex

Physician Assistants (PAs) are a relatively new addition to the Ontario healthcare system. To understand the impact of the PA role, this study investigated supervising physician satisfaction and perception of PA roles, interprofessional team integration, pandemic supports, and barriers and enablers to PA employment. A web-based survey was conducted of 118 physician supervisors of Ontario PA education program alumni. PAs were employed in a variety of community and hospital settings. In addition to patient care, PAs were involved teaching (65.6%), quality improvement (52.7%), and mentorship (40.0%). Overall, 92.9% of physicians indicated they were satisfied with their PAs. Important barriers to hiring PAs included maintaining PA salaries, billing limitations, and PA shortages. PAs have established themselves as valuable and competent members of healthcare teams. By continuing to explore the enablers and barriers to PA employment from the physician perspective, health leaders can continue to optimize and support role integration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.318
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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