An implementation demonstration of Engage, a behavioral intervention for depression, in a geriatric mental health care setting
Bibliographic record
Abstract
OBJECTIVES: Engage is an empirically-supported brief behavioral intervention for later-life depression yet to translate from randomized controlled trials to implementation. This study evaluated a real-world implementation demonstration of Engage across a geriatric mental health care setting. METHOD: The exploration, preparation, implementation, sustainment framework guided this demonstration. Interprofessional case managers received training and ongoing consultation in Engage and applied it with older adults with depression over four months. Upon completion of the implementation trial, providers participated in a 1-h focus group to provide feedback about training, treatment perceptions, and facilitators and barriers to implementation. Focus group transcripts were double-coded using thematic analysis to extract themes informed by the Consolidated Framework for Implementation Research (CFIR). RESULTS: = 21 provider participant respondents) related to CFIR constructs of innovation adaptability, innovation design, critical incidents, compatibility, access to knowledge and information, need, and capability. Findings suggest that Engage is feasible and fits the needs of providers, patients, and the healthcare system. Implementation barriers included depressive symptom burden, patient complexity, and therapist concerns related to self-efficacy and previous experiences. CONCLUSION: Provider feedback can inform and strengthen implementation of evidence-based psychotherapies such as Engage.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".