Adaptação transcultural para a língua portuguesa do The Body-related Self-Conscious Emotions Fitness Instrument (BSE-FIT)
Bibliographic record
Abstract
The scope of this study was to conduct a transcultural adaptation of the Body-Related Self-Conscious Emotions Fitness Instrument (BSE-FIT) to Brazilian Portuguese. The adaptation process involved the following steps: translation of the BSE-FIT to Brazilian Portuguese; evaluation by the Committee of Judges; back-translation; semantic analysis; and pre-test. The members of the Committee highlighted the difficulties in translating the word "fitness" and suggested that the meaning in Brazilian Portuguese is more than "physical form," also relating it to what the body is capable of doing. In the semantic analysis, difficulty was observed in understanding item 4 (proud of my superior physical preparation) and the item was changed to "proud of my enhanced physical preparedness" to facilitate understanding. In the pre-test stage, there were no difficulties in completing or understanding the items and the process of cross-cultural adaptation was finalized. In the final analysis, the semantic, idiomatic, conceptual and cultural equivalence of the adapted instrument was achieved, and basic and content validity parameters were met. However, in order for BSE-FIT to be used in Brazil, it is necessary to validate the metric properties of the BSE-FIT, which is currently under study and being developed.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".