Ontario healthcare workers who sought treatment for their mental health during the first five waves of the COVID-19 pandemic: a snapshot of self-referrals across the province
Bibliographic record
Abstract
INTRODUCTION: Healthcare workers (HCWs) have reported COVID-19 pandemic-related adverse mental health impacts. We examined the demographic profile of HCWs who self-referred for mental health treatment, how referrals changed over time in relation to waves of COVID-19, what the main problem was for which HCWs sought treatment, and how this changed during the pandemic. METHODS: Five major healthcare institutions provided mental health supports to HCWs across Ontario during the pandemic. Data from May 2020 to March 2022 were collected from 2725 HCW self-referrals regarding referral frequency, main presenting mental health problem and demographic information including ethnicity, gender, age, healthcare setting, profession and whether the HCW had a prior mental health diagnosis or had received prior mental health treatment. RESULTS: Treatment-seeking HCWs who self-referred predominantly self-identified as female and White. Almost half were nurses, and almost half had received previous mental health treatment; a slightly higher percentage reported a prior mental health diagnosis. Over 60% of the overall sample of HCWs worked in hospitals. The timing of increases and decreases in monthly new referrals roughly aligned with the onset and ending, respectively, of COVID-19 waves. The top five most common presenting problems for treatment-seeking were generalized anxiety/worry symptoms, depression, situational crisis/acute stress response, difficulty with stress/occupational or financial, and posttraumatic stress symptoms. CONCLUSION: Ontario HCWs self-referred to access mental health supports during the COVID-19 pandemic. The majority sought treatment for generalized anxiety/worry or depression symptoms. Results of this study may inform system planning for future pandemics, as well as for HCW wellness programs for continued workplace stress in the postpandemic period.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".