Impact of group practices on patients, physicians and healthcare systems: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Group practices have potential benefits for patients, physicians and healthcare systems. Although group practices have been around for many years, research in this area is lacking and generally is centred around the economic benefits that may be realised from group practice. The aim of this scoping review is to identify the impact that group practices have on patients, physicians and healthcare systems to guide further research in this area. METHODS AND ANALYSIS: A scoping review will be performed based on the methodology proposed by Arksey and O'Malley and refined by Levac and colleagues. MEDLINE, EMBASE, Cochrane Central and Cochrane Economic Database will be searched from inception to present day to identify relevant studies that assess the impact of group practices on patient care, satisfaction and outcomes; physician quality of life, satisfaction and income and healthcare systems. Titles and abstracts will be screened by two members and the abstraction results charted and verified. Qualitative and quantitative analyses will be performed to identify key themes. ETHICS AND DISSEMINATION: Research ethics board approval is not required for this scoping review. A consultation phase will be used to discuss the results with key stakeholders followed by dissemination at local and national levels. We will also publish the results in a peer-reviewed journal.
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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 | 0.100 | 0.075 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".