Development and Pilot Evaluation of a Tablet-Based Application to Improve Quality of Care in Child Mental Health Treatment
Bibliographic record
Abstract
BACKGROUND: Children need access to high quality mental health care. Effective treatments now exist for a wide range of mental health conditions. However, these interventions are delivered with variable effectiveness in traditional mental health service settings. Innovative solutions are needed to improve treatment delivery quality and effectiveness. OBJECTIVE: The aim of this study was to develop a scalable, sustainable technology-based approach to improve the quality of care in child mental health treatment. METHODS: A tablet-based resource was developed with input from mental health training experts, mental health providers, and patients. A series of qualitative data collection phases (ie, expert interviews, patient and provider focus groups, usability testing) guided the initial concept and design of the resource, and then its refinement. The result was an iPad-based "e-workbook" designed to improve child engagement and provider fidelity in implementation of a best-practice treatment. We are currently conducting a small scale randomized controlled trial to evaluate the feasibility of e-workbook facilitated child mental health treatment with 10 providers and 20 families recruited from 4 local community-based mental health clinics. RESULTS: Usability and focus group testing yielded a number of strong, favorable reactions from providers and families. Recommendations for refining the e-workbook also were provided, and these guided several improvements to the resource prior to initiating the feasibility trial, which is currently underway. CONCLUSIONS: This study aimed to develop and preliminarily evaluate a tablet-based application to improve provider fidelity and child engagement in child mental health treatment. If successful, this approach may serve as a key step toward making best-practice treatment more accessible to children and families. As various technologies continue to increase in popularity worldwide and within the health care field more specifically, it is essential to rigorously test the usability, feasibility, acceptability, and effectiveness of novel health technology solutions. It is also essential to ensure that patients and providers drive decision making that supports the development of these resources to ensure that they can be seamlessly integrated into practice. TRIAL REGISTRATION: Clinicaltrials.gov NCT01915160; https://clinicaltrials.gov/ct2/show/NCT01915160 (Archived by WebCite at http://www.webcitation.org/6cPIiQDpu).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".