Proactively Delivered Digital Mental Health Support for Health Care Workers: Usability and Acceptability Evaluation
Bibliographic record
Abstract
BACKGROUND: Health systems are investing in mental health and well-being support tools and resources for health care workers (HCW). Considering the mental health strain facing HCWs, there is a need to optimize the current mental health delivery model. OBJECTIVE: This study aimed to evaluate the usability and acceptability of a proactive digital mental health approach (Cobalt+;Penn Medicine), which included services proactively sent to HCWs via text messaging, including (1) monthly automated text messaging reminders and links to Cobalt, and (2) bimonthly text-message-based measures of depression and anxiety. METHODS: This study used the System Usability Scale (SUS), Net Promoter Score (NPS), and open-ended questions to capture Cobalt+ participants who received proactive digital mental health tools and resources. Descriptive summary statistics were used for SUS and NPS outcome measures, and a chi-square test was used to detect group differences. Open-ended questions were analyzed using a qualitative open coding process by 2 coders. Research team members calculated interrater agreement (Cohen κ above 0.80). RESULTS: A total of 162 of 642 HCWs randomized to Cobalt+ (25.2%) visited Cobalt due to a proactive text message and completed usability and acceptability measures. The mean age was 38.9 years, most were female (90.7%), 56.8% White, 53.1% married or partnered, and 34.6% engaged in shift work. The mean SUS score was 74.43 (median score 72.5). Participants said they mostly "browsed" the online mental health platform. Cobalt+ received an NPS of 13.7. When asked to elaborate on their experience, 2 categories (eg, positive and negative experiences) with 13 subcategories were identified. Most participants noted the brief process that helped prioritize mental health: "Forget otherwise. Puts in forefront of my mind," and "Your texts do remind me to take stock of my current feelings." CONCLUSIONS: A proactive digital mental health approach may help overcome barriers in the uptake of services that are otherwise passively available to HCWs. This study demonstrated that the proactive approach is generally usable, modestly acceptable, and further supplemented by HCW feedback. These findings suggest the approach's viability and the need for additional research toward improvement and broader implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05028075; https://clinicaltrials.gov/study/NCT05028075.
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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 | 0.030 | 0.035 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".