The Genetic Psychosocial Risk Instrument (GPRI): A Validation Study for European Portuguese
Bibliographic record
Abstract
INTRODUCTION: Screening instruments specifically developed to identify genetic testing applicants who may need professional psychosocial support are much needed. However, there are no screening instruments validated for the Portuguese language. This paper presents the translation, adaptation, and validation process of the Genetic Psychosocial Risk Instrument in a sample of 207 Portuguese applicants to genetic testing in the context of inherited cancer risk. MATERIAL AND METHODS: Participants were mainly female (84.06%), with a mean age of 40.08 (SD = 12.89) and were recruited from the Portuguese Oncology Institute of Porto. Confirmatory factor analysis was conducted to confirm the Genetic Psychosocial Risk Instrument factorial structure. Convergent validity was assessed with the Impact of Events Scale, the Clinical Outcome Routine Evaluation - Outcome Measure, and the Hospital Anxiety and Depression Scale. RESULTS: A model composed by the factors 'Internal Impact of Genetic Testing', 'External Impact of Genetic Testing' and 'History of Mental Health Concerns' was confirmed. These factors showed good internal consistency, convergent and discriminant validity. The factor 'Personal Loss to Cancer' proposed in the Canadian and French versions did not converge. We propose excluding this factor from the European Portuguese version of the scale. CONCLUSION: The European Portuguese version of the Genetic Psychosocial Risk Instrument is a reliable and valid instrument, although more research is needed to effectively use it in routine clinical oncogenetic departments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".