Adaptation and validation of the inventory of psychotic-like anomalous self-experiences (IPASE) into spanish to assess anomalous self-experiences
Bibliographic record
Abstract
Anomalous self-experience, or the experience of the self, are frequently present but underexplored in patients with schizophrenia. Unfortunately, to date, there are no available inventories in Spanish to assess these self-experiences. The present study aims to adapt and validate the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE) in the Spanish population. A total of 171 participants were included: 112 cases (patients) and 59 healthy controls. Among them, 87 patients were diagnosed with schizophrenia (70 chronic and 17 first-episode patients) and 25 patients with bipolar disorder. The participants were evaluated using the structured clinical interview DSM-IV and were tasked with completing the Spanish version of the IPASE. The properties of the scale were analysed in terms of internal consistency, stability, and correlation between scores on the subscales with sociodemographic and clinical variables. The IPASE showed good reliability (Cronbach's alpha coefficient of 0.847) and intraclass correlation,with a value of 0.837 for the patient group and 0.812 for the control group. The variables of age and sex did not significantly correlate with the total IPASE score. Compared to healthy controls, cases obtained significantly higher overall scores on the IPASE and its five subscales; total scores on the IPASE (Cases: (20.96 ± 42.5)vs. control:(80.56 ± 20.6), p < 0.001). The Spanish version of the IPASE scale shows good psychometric properties in terms of reliability and validity for its application in assessing alterations in subjective self-experiences in patients with schizophrenia. This demonstrates the value of the IPASE as a tool in both clinical practice and research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".