Can Geriatric Psychiatry Patients Complete Symptoms Self-Reports Using Tablets? A Randomized Study
Bibliographic record
Abstract
BACKGROUND: With our aging population and limited number of geriatric psychiatrists, innovations must be made in order to meet the growing demands for geriatric psychiatry services. Emerging technologies could greatly improve access to care and systematic data collection. METHODS: This randomized study compared completion rates and time to completion (primary outcomes) when using iPad technology vs. traditional paper forms to complete self-report psychiatric symptoms. Geriatric psychiatry outpatients (n = 72) and adult psychiatry inpatients (n = 50) were recruited to complete the Brief Symptom Inventory (BSI-53), the Activities of Daily Living (ADL), and Patient Health Questionnaire (PHQ-9) questionnaires. RESULTS: = .04) were associated with a shorter time to completion. The effect of questionnaire formats was especially prominent in the inpatient group on time to completion. CONCLUSIONS: Older adults with mental illness demonstrate a similar ability to complete self-report questionnaires whether iPads or paper forms. iPad questionnaires may even require less time to complete in geriatric psychiatry inpatients. Patients also found iPad questionnaires to be easy to use and read. Tablets could potentially be used for psychiatric symptom assessment for clinical, research, and population health purposes.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".