Adaptação transcultural para o português brasileiro do instrumento Indicators of Integration Scorecard
Bibliographic record
Abstract
BACKGROUND: Broad-encompassing approaches to the evaluation, documentation and discussion of workplace health assets are needed to implement effective interventions and promote health and well-being among workers through effective efforts in programs and initiatives to maintain and improve workplace health and safety. OBJECTIVE: To perform the cross-cultural adaptation of the Indicators of Integration (II) scorecard to the Brazilian Portuguese language to enable its use in Brazil and thus provide organizations a means to evaluate the integration of workers' health and well-being programs, policies and practices. METHODS: We followed scientific recommendations described in previous studies and carried out the process of adaptation along six steps: translation, reconciliation of translations, back translation into the original language, revision of the Portuguese version by an internal expert committee, pretest with an expert panel, and final review. RESULTS: The methods applied resulted in an adequate instrument for self-evaluation of the integration of workers' health and well-being programs and practices in organizations; the adapted version conserves the properties of the original. CONCLUSION: (IIPSO) - obtained in the present study is appropriate to be administered and to measure the implementation and integration of health, safety and well-being actions.
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".