Optimization Of Motivation To Improve The Research Performance Of Lecturers In The Midwifery Department
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Motivation from lecturers can improve research performance supported by Good University Governance (GUG) and Supervision in carrying out Research, Revealing data on improving lecturer research performance through motivation optimization. Objectives: The population in this study is all lecturers and education staff in the Midwifery Department of Semarang Poltekkes, consisting of 5 campuses. The sampling was lecturers and education staff who had conducted multi-stage research, as many as 82 people. Methods: This questionnaire is an instrument for Google Forms data Analysis analyzed with Path analysis. Results: significant the influence of GUG on research performance through. Motivation obtained a value of 0.15. The direct impact of Supervision on research performance received effective results with results of 0.929. The indirect influence of Supervision through Motivation on Lecturer Performance is known to have no significant effect, with a value of 0.28. Gug and Supervision through Motivation have no direct impact on research performance. Conclusions: Provide additional theories of Motivation related to the research performance of midwifery lecturers and decision-making in developing human resources in universities, especially in the field of research in the Midwifery Department.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it