Mental Health Care Provider Experiences of Remote Measurement-Based Care Rollout in an Urban Safety-Net Psychiatry Department: Three-Site Mixed Methods Hypothesis-Generating Implementation Study
Bibliographic record
Abstract
BACKGROUND: Measurement-based care (MBC), including remote MBC, is increasingly being considered or implemented for mental health treatment and outcomes monitoring in routine clinical care. However, little is known about the health equity implications in real-world practice or the impact on patient-provider relationships in lower-resource systems that offer mental health treatment for diverse patients. OBJECTIVE: This hypothesis-generating study examined the drivers of MBC implementation outcomes, the implications for health equity, and the impact of MBC on therapeutic alliance (TA). The study was conducted 1 year after the implementation of remote MBC at 3 outpatient adult clinics in a diverse, safety-net health system. METHODS: This explanatory sequential mixed methods study used quantitative surveys and qualitative focus groups with mental health care providers. Repeated surveys were first used to understand mental health care provider experiences over a 6-month period, at least 1 year after MBC implementation. Surveys were analyzed to refine focus group prompts. Six mental health providers participated in repeated surveys over 6 months, after which the same 6 providers and 1 additional mental health provider took part in focus groups. RESULTS: Surveys revealed stable acceptability and utility ratings, concerns that MBC was not equally benefiting patients, little endorsement that MBC improved TA, and slightly decreasing feasibility scores. In focus groups, mental health care providers shared concerns about the acceptability, appropriateness, feasibility, and equity of processes for collecting MBC data. These providers had less first-hand experience with sharing and acting upon the data but still voiced concerns about the processes for doing so. TA both impacted and was impacted by MBC in positive and negative ways. The potential drivers of the findings are discussed using qualitative data. CONCLUSIONS: More than 1 year after the implementation of remote MBC for mental health, mental health care providers had enduring concerns about its implications for health equity as well as its bidirectional relationship with TA. These findings suggest that further study is needed to identify system-level strategies to mitigate potential negative effects of real-world MBC implementations on health equity, particularly in low-resource settings with diverse populations.
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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 | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".