An ergonomic approach to reorganize parking inspection agents' work productivity, health and safety in São Paulo, Brazil
Bibliographic record
Abstract
OBJECTIVE: The Traffic Engineering Company of the City of São Paulo (Brazil) observed a decrease in productivity, and an increase in sick leave, accidents and psychological distress among their parking inspection agents. To document this situation, qualitative research was undertaken to obtain an in-depth comprehension of work activity. PARTICIPANTS: Workers, managers and health and safety professionals contributed to the documentation of the problem and to the proposal of possible solutions. METHODS: Ergonomic work analysis focusing on real work activity, as well as interviews with individual or groups of stakeholders, were conducted. RESULTS: This research revealed that political-economic factors gradually contributed to: 1) an increasing work load; 2) growing fatigue throughout the day, increasing the workers' vulnerability to incidents and accidents and their tendency to react inappropriately to violence experienced on the street; and 3) excessive individual responsibility to manage dangerous situations. CONCLUSIONS: Recommendations to ameliorate the situation are proposed. These suggestions are discussed in terms of feasibility given the impact of macro social factors upon micro work activity, and the associated potential expansion of the ergonomist's role.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".