Beyond frequency: Evaluating the validity of assessing the context, duration, ability, and botherment of depression and anxiety symptoms in South Brazil.
Bibliographic record
Abstract
= 1,871) of adults (66% females, aged 33.4 ± 13.2), weighted to approximate with the state-level population. We examined measurement invariance across the different question frames, estimated whether framing affected mean scores, and tested their independent validity using covariate-adjusted and sample-weighted structural equation models. Validity was tested using tools assessing general disability, alcohol use, loneliness, well-being, grit, and frequency-based questions from depression and anxiety questionnaires. A bifactor model was applied to test the internal consistency of the question frames under the presence of a general factor (i.e., depression or anxiety). Measurement invariance was supported across the different frames. Framing questions as ability (i.e., "How easily …") produced a higher score, compared with framing by context (i.e., "In which daily situations …"). Construct and criterion validity analysis demonstrate that variance explained using multiple question frames was similar to using only one. We detected a strong overarching factor for each instrument, with little variances left to be explained by the question frame. Therefore, it is unlikely that using different adverbial phrasings can help clinicians and researchers to improve their ability to detect depression or anxiety. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".