Validation of the Patient Generated Index for people with severe mental illness.
Bibliographic record
Abstract
Objective: The Patient Generated Index (PGI) is a personalized quality of life (QOL) measure.This secondary analysis examined its psychometric properties with people with severe mental illness.Methods: Three hundred and eleven people with severe mental illness participated in structured interviews at baseline, nine months, and 18 months.Results: The PGI captured a range of self-defined life areas.PGI scores were correlated with measures of QOL, hope, and functioning, indicating concurrent (criterion) validity.The correlation with QOL, with the finding that PGI scores were significantly higher for people who were employed (n = 42) versus unemployed (n = 269) and for people without substance use disorder (n = 269) versus those with substance use disorder (n = 42), is indicative of construct validity. Conclusions and Implications for Practice:The results support the suitability of the PGI as an idiographic measure for monitoring personalized QOL of people with severe mental illness. Impact and ImplicationsThis study provides validation of the use of the PGI as an idiographic, personalized measure of QOL with people with severe mental illness.The individualized nature of the measure makes it pertinent for use in the delivery of recovery-oriented services, providing a way to assess and monitor life domain areas that are specific and important to each person.
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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 | 0.008 | 0.031 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".