A Pilot Study of an Electronic Interprofessional Evidence-Based Care Planning Tool for Clients with Mental Health Problems and Addictions
Bibliographic record
Abstract
BACKGROUND: The health system must develop effective solutions to the growing challenges it faces with respect to individuals who suffer with mental health disorders and addictions. The purpose of this study was to evaluate the usability and potential impact on outcomes of a knowledge translation system aimed at improving client-centered, evidence-based care for hospitalized individuals with schizophrenia. METHODOLOGY: A pre-posttest design was used. The e-Volution-TREAT system was implemented on two inpatient units at a large mental health facility. Thirty-seven nurses, allied health workers, and physicians participated from two units. Data collection involved questionnaires, semistructured interviews, and observations. Thirty-eight consenting clients' outcome data were collected from organizational records. RESULTS: Overall, staff participants were very satisfied with the functions of the e-Volution-TREAT system. Barriers to using the system were identified by participants related to the work environment, to understaffing, equipment problems, discomfort with technology, and a focus on short-term rather than long-term goals. There was moderate uptake of guidelines related to social issues, and low uptake of guidelines related to family support and addictions. There were significant improvements in four client outcomes over time, specifically aggressive behavior, depression, withdrawal, and psychosis. CONCLUSIONS: In conclusion, users were overall satisfied with the e-Volution-TREAT system, although expressed challenges related to workload that interfered with time to utilize the system. It would be premature to conclude the change in client outcomes was related to the e-Volution-TREAT system without a randomized controlled trial with outcomes compared to a control group. Future research needs to incorporate strategies for modifying the context and engage clinicians who are in a position of influence to model change.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".