Creation of an OHS knowledge portal: an action research
Bibliographic record
Abstract
Purpose Research findings stress the importance of adapting prevention mechanisms to the contexts experienced in the workplace. This paper presents the development and implementation of a knowledge portal that includes a range of Internet-based resources to support the prevention measures implemented by occupational health and safety (OHS) union delegates. It describes the process used to develop a knowledge portal that takes into account the needs of communities and unions as well as the constraints expressed. Design/methodology/approach The approach chosen for this project was action research, in which data collection results in various readjustment loops that allow for reflection and situational assessment. Data were collected from documentation, meetings, questionnaires and focus groups. The readjustment loops led to the implementation of a solution based on sustainability. Findings After studying the context, needs and constraints, the results suggest that for a knowledge portal to stand out, it must be consistent with classroom training, include a pedagogical approach that facilitates the transfer of knowledge, be interesting to all workers, be able to adapt to the characteristics of users and use technologies that reach across time, space and connection tools. Originality/value This knowledge portal is the result of interactions and collaborations between the university and the community, an interesting way to develop a solution. It sheds light on the fact that the action research process needs to be documented throughout the process and creation cycles in order to facilitate the sharing of the results obtained.
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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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".