Conceptual Model for the Development of Employee Competencies Through the Well-Being Implementation
Bibliographic record
Abstract
Modeling the process of developing employee competencies and assessing their impact on an organization's performance is an urgent task.The study aims to develop a unified concept for modeling the process of employee competency development by implementing a corporate well-being program to achieve the workers' target KPIs.The study consisted of two stagesmodeling and a survey.A database on the components of the model is formed based on a survey of 727 individuals from different companies and economic sectors.The model is tested by means of preliminary analysis of the collected data, their clustering, assessment of interrelations of components, and systematization of the existing regularities.Fuzzy clustering of values, well-being elements, and individuals is constructed on multidimensional samples.Estimates of the probabilities of elements' transitions from clusters by values to clusters by activities and vice versa are obtained.The fuzzy clustering algorithm is developed in Python.The results show that for employees with a less pronounced value model, the well-being program in the company is of medium importance.Conversely, the well-being program in the company is of high importance for employees with prevailing social values.Employee clustering can suggest several propositions for the most efficient activities of the corporate well-being program according to the envisioned generalized employee value model.Conversely, it can help determine a candidate's optimal value profile for them to work effectively in the organization proceeding from the current corporate well-being program.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".