Exploring surgeon behavioural factors impacting the quality of care: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Modern surgery incorporates many aspects of care, including preoperative workup, surgical management and multidisciplinary collaboration to achieve favourable outcomes and high patient satisfaction. Current literature identifies variability in surgical practice and quality of care. The objective of this study is to fill the gap in the literature by identifying modifiable surgeon behavioural factors influencing the quality of care and to identify interventions and policies that modify these factors. METHODS AND ANALYSIS: This scoping review will be reported using Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review guidelines. The protocol was drafted according to JBI Best Practice Guidance and Reporting Items for the Development of Scoping Review Protocols. A comprehensive search encompassing five databases (OVID Medline, OVID EMBASE, Cochrane Library (Central) and SCOPUS) was conducted. Search terms included 'surgeons', 'surgeon characteristics', 'quality of care' and 'outcomes' using AND, OR and ADJ2 Boolean operators. Studies describing interventions aimed at modifying behavioural surgeon factors influencing the quality of healthcare will be included. Studies describing institutional or system factors will be excluded. Searches were limited from 1 January 2000 to 1 January 2024 to capture modern surgery practices. Searches were peer reviewed as per Peer Review of Electronic Search Strategies 2015. Two independent reviewers will perform a title and abstract screening using DistillerSR and extract data on the participants, study methods, modifiable surgeon factors and interventions that modify these factors. The data will be qualitatively analysed using the COM-B Framework which describes how capability, motivation and opportunity constitute behaviour. We expect to compile a list of existing interventions aimed at modifying surgeon behaviours, analysing the success of existing interventions to improve patient outcomes and identifying modifiable surgeon factors that do not have interventions. ETHICS AND DISSEMINATION: Ethics approval and patient consent are not required. The results will be submitted to a peer-reviewed journal for publication.
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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 | 0.094 | 0.120 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.088 | 0.016 |
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".