Identification and Prioritization of Factors Affecting Knowledge Concealment By Managers in Guilan University of Medical Sciences by AHP Hierarchical Method
Bibliographic record
Abstract
Background and Aim Hiding knowledge in the organization is a new topic in the field of knowledge management. The purpose of this study is to identify and prioritize the factors affecting knowledge concealment by managers in Guilan University of Medical Sciences by AHP hierarchical method. Methods & Materials The research is applied in terms of purpose and based on a qualitative-quantitative approach. The statistical population of the study included philosophical experts in the field of management and experimental experts of the University of Medical Sciences who were selected as a sample by purposive sampling method and snowball technique until the theoretical saturation was reached. The data collection tool was a semi-structured interview. After enumerating the indicators affecting knowledge concealment and then by hierarchical analysis (AHP) method and using Expert Choice11 software, the identified factors were prioritized. Ethical Considerations In this research, prior to interviewing the experts, written consent was received from them regarding the confidentiality of the research (Code: IR.IAU.LIAU.REC.1401.002). Results The research findings showed that among the 7 effective factors considered by experts, the power-seeking factor has the greatest impact on knowledge concealment by managers in the University of Medical Sciences and emotional intelligence has the least weight or importance. Conclusion Accordingly, by reducing power-seeking, in addition to creating a transparent and reliable atmosphere, it is possible to establish knowledge sharing in the University of Medical Sciences.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".