Integrating Smartphone Technology at the Time of Discharge from a Child and Adolescent Inpatient Psychiatry Unit.
Bibliographic record
Abstract
OBJECTIVE: As smartphone technology becomes an increasingly important part of youth mental health, there has been little to no examination of how to effectively integrate smartphone-based safety planning with inpatient care. Our study sought to examine whether or not we could effectively integrate smartphone-based safety planning into the discharge process on a child and adolescent inpatient psychiatry unit. METHOD: ) at the time of discharge. RESULTS: app prior to discharge, with most (68%) suggesting they would download the app after discharge. Notably, all patients who downloaded the app prior to discharge were on their first admission to a psychiatric inpatient unit. CONCLUSION: Child and adolescent psychiatric inpatients have a clear interest in smartphone-based safety planning. Our results suggest that integrating smartphone-related interventions earlier in an admission might improve access before discharge. This highlights the tension between restricting and incorporating smartphone access for child and adolescent inpatients and may inform future study in this area.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".