Translation and cross-cultural adaptation of the Dignity Therapy Question Protocol to Brazilian Portuguese
Bibliographic record
Abstract
OBJECTIVES: Dignity therapy (DT) was developed to help patients at their end of life to reframe and give meaning to their illness process. The DT question protocol focuses on personhood and important aspects of the individual's life. This study aimed to translate and culturally adapt the Dignity Therapy Question Protocol (DTQP) to Brazilian Portuguese. METHODS: This was a descriptive and methodological study, and cross-cultural adaptation process comprised 4 stages: (1) translation and synthesis of English original version protocol into Brazilian Portuguese, (2) back translation, (3) experts committee, and (4) pretest. RESULTS: - demonstrated a content validity index of 1 for all equivalences. The initial sample consisted of 41 participants (9 [21.9%] refused to participate and 1 [2.43%] dropped out). The pretest was applied to 30 (73.1%) participants, 15 of them were female and the mean age was 53.4 years. The final version consisted of 10 questions that were approved by the original authors who affirmed that the DTQP Brazilian Portuguese version maintained the original English characteristics. SIGNIFICANCE OF RESULTS: The Brazilian cultural adaptation of the DTQP was well understood by patients. It will be very useful in palliative care clinical practice for patients nearing end of life. The adapted version to Brazilian Portuguese will facilitate future studies using the DTQP.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".