The Validation of the Chinese (Cantonese) Version of the Patient Dignity Inventory in a Hong Kong Palliative Care Setting
Bibliographic record
Abstract
Context: To assess and address a patient's dignity and dignity-related distress would greatly benefit patients who have advanced stage disease. The Patient Dignity Inventory (PDI) allows clinicians to identify sources of dignity-related distress for patients. The PDI should be evaluated for use in a local Chinese setting. Objectives: To validate the Patient Dignity Inventory Hong Kong-Chinese (Cantonese) version (PDI-HK) and assess the psychometric properties in patients in an inpatient palliative setting in Hong Kong. Method: The English version of the PDI was translated and back translated, then reviewed by a panel including a clinician, clinical psychologist, and nurse clinician. Recruited patients would complete the PDI-HK, the Chinese version of Hospital Anxiety and Depression Scale (HADS), the McGill Quality of Life Questionnaire-Hong Kong (MQOL-HK), and the Edmonton Symptom Assessment Scale. Psychometric properties including internal consistency, concurrent validity, test-retest reliability, and factor analysis were tested. Results: < 0.001). Concurrent validity with the HADS and MQOL-HK questionnaire was established. Factor analysis showed four factors, namely Existential Distress, Physical Change and Function, Psychological Distress, and Support. These were similar to previous PDI validation studies. Conclusion: The PDI was translated into Chinese (Cantonese) and applied in an inpatient palliative care unit in Hong Kong, with adequate validity. The PDI-HK version can be further used in a larger Chinese population to assess and address dignity-related issues.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".