Can Lean Thinking enhance research administration?
Bibliographic record
Abstract
Background: Hospital research administration is expected to provide improved work efficiency and service delivery in the upcoming years. Meanwhile, resources to support scientific research are dwindling, as is the amount of research support personnel. The aim of the study was to investigate the research admission’s user needs, their opinions on the state of today’s scientific research and also to assess its future development needs and find out whether a customer-oriented management philosophy such as Lean Thinking could be implemented in the research administration setting to enhance work processes and services. Methods: In this study, thematic interviews were used as a method. Kuopio University Hospital researchers (n = 7) and research administration personnel (n = 11) were interviewed. The interviews were carried out by one researcher and lasted on average approximately 60 minutes per interviewee. Results: The interviews revealed that several issues, such as bureaucracy, decreased attitudes towards research, lack of transparency and face-to-face customer service by the research administration, have hindered and complicated research work and service delivery. The study addressed a need for a comprehensive, linear, efficiency- and quality-oriented, user-focused management philosophy. Conclusions: The results of the study support implementation of a customer-oriented management philosophy into the research administration to improve its work processes and service delivery. Although Lean Thinking remains untested in the research administration setting, its fundamentals and strengths seem to answer many needs of the research administration.
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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 | 0.068 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".