Computerized ambulatory monitoring in psychiatry: a multi‐site collaborative study of acceptability, compliance, and reactivity
Bibliographic record
Abstract
Computerized ambulatory monitoring overcomes a number of methodological and conceptual challenges to studying mental disorders, however concerns persist regarding the feasibility of this approach with severe psychiatric samples and the potential of intensive monitoring to influence data quality. This multi-site investigation evaluates these issues in four independent samples. Patients with schizophrenia (n = 56), substance dependence (n = 85), anxiety disorders (n = 45), and a non-clinical sample (n = 280) were contacted to participate in investigations using computerized ambulatory monitoring. Micro-computers were used to administer electronic interviews several times per day for a one-week period. Ninety-five percent of contacted individuals agreed to participate in the study, and minimum compliance was achieved by 96% of these participants. Seventy-eight percent of all programmed assessments were completed overall, and only 1% of micro-computers were not returned to investigators. There was no evidence that missing data or response time increased over the duration of the study, suggesting that fatigue effects were negligible. The majority of variables investigated did not change in frequency as a function of study duration, however some evidence was found that socially sensitive behaviors changed in a manner consistent with reactivity.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".