Multilevel analysis of the determinants of the global assessment of functioning in an inpatient population
Bibliographic record
Abstract
BACKGROUND: The Global Assessment of Functioning (GAF) is a widely used measure of psychiatric symptoms and functioning, yet numerous concerns persist about its reliability and validity. The objective of this study was to determine the extent to which GAF scores reflect physician-related differences in addition to information about patients. METHODS: This is a secondary analysis of clinical data collected between 2005 and 2010 from inpatients at a psychiatric hospital (N = 1,852). Multilevel modeling was used to estimate the influence of physicians on GAF scores at admission and on the change between admission and discharge, controlling for patient clinical presentation. RESULTS: Controlling for patient-level predictors, 7% of the residual variance in admission GAF scores and 8% of the residual variance in change scores was at the physician level. The physician-level variance was significantly larger than zero in both models. CONCLUSIONS: Although statistically significant, estimates of physician-level variance were not overwhelming, suggesting that the GAF was rated in a consistent manner across physicians in this hospital. While results lend support to the utility of the GAF for drawing comparisons between patients seen by different physicians across a large institution, further study is necessary to determine generalizability and to assess differences across multiple institutions.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".