The Italian versions of the This Is ME Questionnaire and the Patient Dignity Question: Understanding personhood and supporting dignity in patients with terminal cancer
Bibliographic record
Abstract
OBJECTIVES: Patients with cancer at the end of life may suffer from high psychological distress, a sense of demoralization, and a lack of dignity related to their medical condition. The This Is ME (TIME) Questionnaire and the Patient Dignity Question (PDQ) are clinical tools developed to achieve comprehensive and personalized patient care and to deepen our understanding of personhood. The objective of this study was to translate and validate the TIME Questionnaire, which contains the PDQ, into Italian to evaluate patient satisfaction of the Italian version of these tools and to identify essential themes elicited by the tools. METHODS: The validation process consisted of a forward and back translation stage, data collection from a sample of 60 patients with terminal cancer, and a final consultation with a panel of experts to identify patient themes using the results of the tool. RESULTS: Overall, participants felt that the PDQ/TIME questionnaire captured their essence as a person, allowed them to express their values and beliefs, and helped the health care professionals (HCP) to take better care of them. Content analysis identified "family relationships," "global pain," and "family roles and accomplishments" as being of most importance to patients. SIGNIFICANCE OF RESULTS: The Italian versions of the PDQ/TIME Questionnaire are clear, precise, understandable, and focused on understanding personhood in patients with advanced cancer. These tools should be used to proactively enhance patient-caregiver and patient-HCP relationships and to develop new perspectives of patient care focused on the critical dimension of personhood.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".