Towards the development of a serious game that targets psychological stressors of the workplace
Bibliographic record
Abstract
In addition to managerial parameters, various factors, which are related to the evolution of modern societies, generate many changes in the world of work. Although they represent great opportunities in themselves, when mishandled, these changes may represent significant stressors in the workplace. As a result, a large proportion of workers in industrialized countries have to cope with psychological distress episodes. Considering that some observations suggest that the first step in targeting stress should be in identifying the stressors that negatively affect the person. Moreover, since serious games have been proved effective for the transfer of knowledge, our work aims at designing a serious game that may help people at identifying stressors of the workplace. As a first step, we report the main elements required for such a game. For this, we use two frameworks that allow us to target the two main aspects of this serious game. This systematic approach offers two main advantages. First, it insures that the designed software solution has the principal elements of a game. Secondly, it supports the implementation of the pedagogical objectives throughout the game mechanics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".