Evaluation of Effective Factors on the Clinical Performance of General Surgeons in Tehran University of Medical Science, 2015
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Existence of doctors with high performance is one of the necessary conditions to provide high quality services. There are different motivations, which could affect their performance. Recognizing Factors which effect the performance of doctors as an effective force in health care centers is necessary. The aim of this article was evaluate the effective factors which influence on clinical performance of general surgery of Tehran University of Medical Sciences in 2015. METHODS: This is a cross-sectional qualitative-quantitative study. This research conducted in 3 phases-phases I: (use of library studies and databases to collect data), phase II: localization of detected factors in first phase by using the Delphi technique and phase III: prioritizing the affecting factors on performance of doctors by using qualitative interviews. RESULTS: 12 articles were analyzed from 300 abstracts during the evaluation process. The output of assessment identified 23 factors was sent to surgeons and their assistants for obtaining their opinions. Quantitative analysis of the findings showed that "work qualification" (86.1%) and "managers and supervisors style" (50%) have respectively the most and the least impact on the performance of doctors. Finally 18 effective factors were identified and prioritized in the performance of general surgeons. CONCLUSION: The results showed that motivation and performance is not a single operating parameter and it depends on several factors according to cultural background. Therefore it is necessary to design, implementation and monitoring based on key determinants of effective interventions due to cultural background.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".