Reducing the length of mental health instruments through structurally incomplete designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents structurally incomplete designs as an approach to reduce the length of mental health tests. In structurally incomplete test designs, respondents only fill out a subset of the total item set. The scores on the unadministered items are estimated using methods for missing data. As an illustration, structurally incomplete test designs recording, respectively, two thirds, one half, one third and one quarter of the complete item set were applied to item scores on the Centre of Epidemiological Studies-Depression (CES-D) scale of the respondents in the Longitudinal Aging Study Amsterdam (LASA). The resulting unobserved item scores were estimated with the missing data method Data Augmentation. The complete and reconstructed data yielded very similar total scores and depression classifications. In contrast, the diagnostic accuracy of the incomplete designs decreased as the designs had more unobserved item scores. The discussion addresses the strengths and limitations of the application of incomplete designs in mental health research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it