Impact of the COVID-19 pandemic on Canadian performing and creative artists: An interpretive descriptive study using the social-ecological model
Bibliographic record
Abstract
BACKGROUND: Public health restrictions during the Coronavirus-2019 (COVID-19) pandemic in Canada have substantially reduced the work and income of performing and creative artists. We aimed to understand how factors at the public policy, community, organizational, interpersonal and individual levels affected Canadian performing and creative artists' health and livelihood during the pandemic. METHODS: We interviewed 14 creative and performing artists from an academic hospital-based healthcare center in Toronto, Canada. In addition, we conducted secondary data analysis on an existing set of 17 transcribed interviews from a quality improvement study that included relevant information to answer the present study's research question. We applied an interpretive descriptive approach to our qualitative inquiry and used the social-ecological model (SEM) as our analytic framework. RESULTS: We identified factors at all levels of the SEM that tended to synergistically affect the health and livelihood of artists during the COVID-19 pandemic. Public health restrictions and government financial assistance programs have downstream effects on other levels. During the pandemic, many artists sensed an overwhelming loss of community, financial instability, and limited access to healthcare; which in turn affected their health. For those who accessed financial assistance programs, the stability of income afforded time for rest without the stress of food insecurity or housing instability. CONCLUSIONS: Use of the SEM as an analytic framework reflects the multidirectional intricacy and dynamic interplay among factors operating within and across all five levels, bringing to light potential areas of improvement at various levels to strengthen resilience and reduce risk factors associated with artists' health and healthcare access. Findings also accentuated the fragility of precarious work that inundates the performing arts industry, which emphasizes the need for interventions and policies to address this issue. Such interventions might include financial support programs for artists, access to affordable healthcare services, and efforts to strengthen social support networks within the arts community.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 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".