Using ecological momentary assessment for patients with psychosis posthospitalization: Opportunities for mobilizing measurement-based care.
Bibliographic record
Abstract
OBJECTIVE: Ecological momentary assessment (EMA) via mobile devices offers a promising approach for collecting real-time data from psychiatric patients, potentially as an augment to traditional measurement-based care strategies. This study examined whether EMA had added value in collecting clinically important data from recently hospitalized adults with psychosis, relative to traditional assessments. METHOD: In a sample of 24 adults with psychosis, EMA data regarding psychotic symptoms, affect, alcohol and drug use, functioning, quality of life, and social support were collected starting immediately posthospital discharge and extending for up to one month during their transition to outpatient care. EMA data were compared with traditional retrospective assessments of the same constructs, administered at a 1-month follow-up assessment. RESULTS: Data from EMA and traditional retrospective assessments were correlated with each other in most cases. However, in some cases, participants were more likely to report drug use, medication nonadherence, and psychotic symptoms via EMA compared with traditional retrospective assessments. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Results suggest that the additional information obtained via frequent in-the-moment self-reports collected using smartphones can provide an expanded picture of individuals' symptomatic and functional experiences. Thus, monitoring patients' progress posthospitalization could be improved through the use of EMA. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".