Examining the Utility of a Telehealth Warm Handoff in Integrated Primary Care for Improving Patient Engagement in Mental Health Treatment: Randomized Video Vignette Study
Bibliographic record
Abstract
BACKGROUND: A warm handoff from a physician to a mental health provider is often patients' first contact with psychological services and provides a unique opportunity for improving treatment engagement in integrated primary care (IPC) settings. OBJECTIVE: In light of the COVID-19 pandemic, this study sought to examine the impact of different types of telehealth mental health referrals on both the anticipated likelihood of accepting treatment services and anticipated likelihood of continued treatment engagement. METHODS: A convenience sample of young adults (N=560) was randomized to view 1 of 3 video vignettes: warm handoff in IPC, referral as usual (RAU) in IPC, or RAU in standard primary care. RESULTS: =32.6, P<.001) were significant. Participants who received a warm handoff were significantly more likely to anticipate both accepting the referral (b=0.35; P=.002; odds ratio 1.42, 95% CI 1.15-1.77) and engaging in continued treatment (b=0.62; P<.001; odds ratio 1.87, 95% CI 1.49-2.34) compared with those who received RAU in the standard primary care condition. Furthermore, 77.9% (436/560) of the sample indicated that they would be at least somewhat likely to access IPC mental health services for their own mental health concerns if they were readily available in their own primary care physician's office. CONCLUSIONS: A telehealth warm handoff resulted in the increased anticipated likelihood of both initial and continued engagement in mental health treatment. A telehealth warm handoff may have utility in fostering the uptake of mental health treatment. Nonetheless, a longitudinal assessment in a primary care clinic of the utility of a warm handoff for fostering referral acceptance and continued treatment engagement is needed to hone the adoptability of a warm handoff process and demonstrate practical evidence of effectiveness. The optimization of a warm handoff would also benefit from additional studies examining patient and provider perspectives about the factors affecting treatment engagement in IPC settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".