“Don't Just Study our Distress, Do Something”: Implementing and Evaluating a Modified Stepped-Care Model for Health Care Worker Mental Health During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Throughout the COVID-19 pandemic, there have been concerns about the mental health of health care workers (HCW). Although numerous studies have investigated the level of distress among HCW, few studies have explored programs to improve their mental well-being. In this paper, we describe the implementation and evaluation of a program to support the mental health of HCW at University Health Network (UHN), Canada's largest healthcare network. METHODS: Using a quality improvement approach, we conducted a needs assessment and then created and evaluated a modified stepped-care model to address HCW mental health during the pandemic. This included: online resources focused on psychoeducation and self-management, access to online support and psychotherapeutic groups, and self-referral for individual care from a psychologist or psychiatrist. We used ongoing mixed-methods evaluation, combining quantitative and qualitative analysis, to improve program quality. RESULTS: The program is ongoing, running continuously throughout the pandemic. We present data up to November 30, 2021. There were over 12,000 hits to the UHN's COVID mental health intranet web page, which included self-management resources and information on group support. One hundred and sixty-six people self-referred for individual psychological or psychiatric care. The mean wait time from referral to initial appointment was 5.4 days, with an average of seven appointments for each service user. The majority had moderate to severe symptoms of depression and anxiety at referral, with over 20% expressing thoughts of self-harm or suicide. Post-care user feedback, collected through self-report surveys and semistructured interviews, indicated that the program is effective and valued. CONCLUSIONS: Development of a high-quality internal mental health support for HCW program is feasible, effective, and highly valued. By using early and frequent feedback from multiple perspectives and stakeholders to address demand and implement changes responsively, the program was adjusted to meet HCW mental health needs as the pandemic evolved.
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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 | 0.053 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".