The impact of COVID-19 on the mental health and substance use health (MHSUH) workforce in Canada: a mixed methods study
Bibliographic record
Abstract
Abstract Background The increased need for mental health and substance use health (MHSUH) services during the COVID-19 pandemic underscores the need to better understand workforce capacity. This study aimed to examine the pandemic’s impact on the capacity of MHSUH service providers and to understand reasons contributing to changes in availability or ability to provide services. Methods We conducted a mixed method study including a pan-Canadian survey of 2177 providers of MHSUH services and semi-structured interviews with 13 key informants. Survey participants answered questions about how the pandemic had changed their capacity to provide services, reasons for changes in capacity, and how their practice had during the pandemic. Thematic analysis of key informant interviews was conducted to gain a deeper understanding of the impact of the pandemic on the MHSUH workforce. Results Analyses of the survey data indicated that the pandemic has had diverse effects on the capacity of MHSUH workers to provide services: 43% indicated decreased, 24% indicated no change, and 33% indicated increased capacity. Logistic regression analyses showed that privately funded participants had 3.2 times greater odds of increased capacity (B = 1.17, p < 0.001), and participants receiving funding from a mix of public and private sources had 2.4 times greater odds of increased capacity (B = 0.88, p < 0.001) compared to publicly funded participants. Top reasons for decreases included lockdown measures and clients lacking access or comfort with virtual care. Top reasons for increases included using virtual care and more people having problems relevant to the participant's skills. Three themes were constructed from thematic analysis of key informant interviews: the differential impact of public health measures, long-term effects of pandemic work conditions, and critical gaps in MHSUH workforce data. Conclusions The COVID-19 pandemic has had a substantial impact on the capacity of the MHSUH workforce to provide services. Findings indicate the importance of increasing and harmonizing funding for MHSUH services across the public and private sectors, developing standardized datasets describing the MHSUH workforce, and prioritizing equity across the spectrum of MHSUH services.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".