Optimization Of Motivation To Improve The Research Performance Of Lecturers In The Midwifery Department
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.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".