Psychometric properties of the Patient Dignity Inventory in an acute psychiatric ward: an extension study of the preliminary validation
Bibliographic record
Abstract
Background: During the last decades, dignity has been an emerging issue in mental health since its ethical and therapeutic implications became known. This study is an extension of the preliminary validation of the Patient Dignity Inventory (PDI) in a psychiatric setting, originally designed for assessing perceived dignity in terminal cancer patients. Methods: From October 21, 2015 to December 31, 2016, we administered the Italian PDI to all patients hospitalized in an acute psychiatric ward, who provided their consent and completed it at discharge (n=165). We performed Cronbach's alpha coefficient and principal factor analysis. We administered other scales concomitantly to analyze the concurrent validity of PDI. We applied stepwise multiple linear regression to identify the patients' demographic and clinical variables related to the PDI score. Results: Our response rate was 93%, with excellent internal consistency (Cronbach's alpha coefficient=0.94). The factorial analysis showed three factors with eigenvalue >1, which explained >80% of total variance: 1) "loss of self-identity and anxiety for the future", 2) "concerns for social dignity and spiritual life", and 3) "loss of personal autonomy". The PDI and the three factor scores were positively and significantly correlated with the Hamilton Scales for Depression and Anxiety but not with other scale scores. Among patients' variables, "suicide risk" and "insufficient social and economic condition" were positively and significantly correlated with the PDI total score. Conclusion: The PDI can be a reliable tool to assess patients' dignity perception in a psychiatric setting, which suggests that both social and clinical severe conditions are closely related to dignity loss.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".