Credentialing of surgeons: a systematic review across a number of jurisdictions
Bibliographic record
Abstract
BACKGROUND: The purpose of credentialing is to ensure that clinicians provide safe, high-quality health-care services in accordance with good practice and legal requirements. This review assessed the institutional credentialing processes and governance structures required to support credentialing processes at an institutional, regional or health-care system level. METHODS: Searches of MEDLINE, EMBASE and PubMed were conducted. Additional grey literature searches were performed using the Google search engine and specific searches of government web sites were conducted. The inclusion criteria were developed a priori and standardized extraction of the information to appraise the research questions was conducted systematically. RESULTS: A total of 33 white papers were included in this systematic literature review: 18 were published in Australia, 1 in New Zealand, 10 in the United Kingdom, 2 in the United States of America and 2 in Canada. Four key principles were common throughout all studies included in this review: clear lines of responsibility for the credentialing process and supportive governance structures, clear standards for credentialing, a culture of continuous improvement and evaluation of credentialing process outcomes. CONCLUSIONS: No data were available to evaluate the relationship between the credentialing process and the safety and quality of health-care services or patient outcomes; and capturing such data is difficult because of the numerous factors that affect the relationship between credentialing, patient outcomes, and the safety and quality of health-care services. Consequently, developing methods to measure the effectiveness of credentialing processes represents an area for further research.
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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".