Bibliographic record
Abstract
Abstract Kessler k6 psychological distress scores are analyzed using a count model and item response theory (IRT) models are applied to the items which produce the k6 score and generate an alternative distress score, θ*. Other ways of utilizing the constituent items are also examined. The data used in the analysis comes from the 2014 National Survey of Drug Use and Health. Three important results emerge. First, θ* and k6 are not highly correlated and their distributions are quite different. The k6 score gives a much more favourable picture of mental health than θ*. Second, k6 does a much better job in explaining participation in treatment programs than θ* suggesting a very limited role for IRT methods in the analysis of psychological distress data. As a diagnostic tool k6 is an effective and simple way of summarizing the item data. Third, for researchers interested in which individual characteristics determine psychological distress better results are obtained by analyzing the six constituent items which are used to generate the k6 score using ordered probability models rather than k6 itself.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".