Screening for cross-cultural adaptations of the Patient’s Dignity Inventory
Bibliographic record
Abstract
Cross-cultural adaptation is a process that involves the transfer of knowledge between different cultures. Therefore, for a psychological instrument to be used in another country, for example, it is necessary to follow methodological rigors for an effective final model. In the field of oncology, research on the concept of dignity is incipient in most of the countries and one of the precursors of this concept was the Canadian psychiatrist, Harvey Chochinov. A model called the Dignity Model was developed and resulted in an inventory (Patient Dignity Inventory). The objective of this research is to carry out a screening on the cross-cultural adaptation studies of the Patient's Dignity Inventory. It is an integrative literature review to verify the main studies published databases about validation of the Patient Dignity Inventory. MEDLINE, LILACS, Scielo and Google Scholar databases were used to track adaptation studies. The keywords "Patient Dignity Inventory" AND "Validation" OR "Cross Cultural" were used for the collection of articles. In the initial results, 121 articles were found. After applying all filters, 19 articles were found within the criteria selected for review. It was noticed that most of the studies used rigorous methods, resulting in inventories with satisfactory psychometric properties for use in another culture.
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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 | 0.048 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".