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Record W7126358836

Patient satisfaction with physician assistants outside of the US

2021· other· en· W7126358836 on OpenAlexaboutno aff
Tenzin Tehshar

Bibliographic record

VenueOpenBU (Boston University) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceHealth careIncentiveAlliancePatient satisfactionPhysician assistantsQuality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

There is a growing disparity between the demand and supply for health workers globally which is projected to result in a health worker shortage of 15 million by 2030. The growing disparity is fueled by low health expenditure budgets, urban migration, weak incentives for entering the healthcare field, and an aging global population. The Global Health Workforce Alliance has suggested mid-level providers, like the physician assistant, as a part of the solution to the looming global shortage. However, physician assistants are not internationally recognized, which creates a challenge for implementing the profession in countries not familiar with the profession. The lack of international recognition has also made it difficult for institutions like the World Health Organization to acquire data on international physician assistant impact. Physician assistant implementation additionally faces pushback from traditional health professions. The physician assistant concept has been adopted in over 15 countries. The international adoption of the profession is relatively recent compared to the profession’s inception in the United States in the 1960s. Therefore, there is a dearth in data on the international impact of physician assistants. Patient satisfaction is an important measure for quality of care and health system performance. The few existing studies on patient satisfaction indicate that patients are satisfied with care provided by physician assistants in Canada, the United Kingdom, Ireland, Australia, New Zealand, and the Netherlands. However, there are significant design flaws in the limited research. The design flaws include not specifying which health worker was in question, use of fictional patient- provider scenarios, and use of qualitative data that can result in misleading conclusions. Thus, this proposed study will incorporate a study design that addresses design flaws in the existing research on patient satisfaction with physician assistants outside of the United States. The study will be a large-scale quantitative standardized patient satisfaction survey that will be disbursed in Canada and the United Kingdom. The study aims to measure patient satisfaction with different elements of the patient-provider encounter as well as the overall quality of care provided by the physician assistant. The study also aims to measure willingness of patients to see a physician assistant at the patient’s next medical provider encounter. Physician assistants can help to reduce the global health workforce shortage. The proposed study will address the need for understanding how patients are perceiving the impact of physician assistants during global expansion of the profession. If the findings from the proposed study align with findings from existing research, then one can infer that there is growing consumer demand for physician assistants. Evidence of growing consumer demand may influence policy surrounding acceptance and recognition of physician assistants internationally, and therefore serve as the catalyst for deploying physician assistants to help address the global health workforce shortage.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.008
GPT teacher head0.187
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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