Bibliographic record
Abstract
It is aimed toincrease the quality of the services provided by applications for accreditationprograms in health services. Initial work on healthcare accreditation began inthe US, Canada and Australia, becoming a tool used in other countries,especially towards the end of the 90's. Prior to the accreditationsystem for health services carried out by TÜSKA in our country, there arequality studies carried out by the Ministry of Health, which is long-term. Thedevelopment of quality culture in health institutions has revealed the need foraccreditation and the work towards this has been increased. TÜSKA develops anddevelops various accreditation programs according to the needs and prioritiesof our country by using the standards and auditor training program accreditedby The International Society for Quality in Health Care (ISQua) and carries outthe activities of receiving, evaluating and conducting the applications to theseprograms. Primarily, the Hospital Accreditation Program has been initiated.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".