Adaptação transcultural para o Brasil do instrumento Caregiver Abuse Screen (CASE) para detecção de violência de cuidadores contra idosos
Bibliographic record
Abstract
This first of two papers focuses on the first part in the cross-cultural adaptation of the Portuguese-language version of Caregiver Abuse Screen (CASE), a brief instrument for detecting domestic violence against the elderly. CASE was originally developed in Canada and used to screen violence against the elderly by interviewing their caregivers. Besides a broad literature review, the evaluation of conceptual and item equivalences involved expert discussion groups. Semantic equivalence included the following steps: two translations and respective back-translations; an evaluation of referential and general (connotative) equivalence between the original instrument and each version; further discussions with experts in order to define the final version; and pre-testing the latter in 40 caregivers of elderly subjects in an outpatient geriatric clinic. It was possible to establish high-quality conceptual, item, and semantic equivalence for the Portuguese-language version. Although the results shown here were encouraging, they should be reevaluated in light of a forthcoming psychometric analysis (measurement equivalence) to be performed by the research group.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".