How anxious were Quebec healthcare professionals during the first wave of the COVID-19 pandemic? A web-based cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic may cause significant anxiety among healthcare professionals (HCPs). COVID-19-related psychological impacts on HCPs in Western countries have received relatively little attention. OBJECTIVE: This study aims to assess the levels of anxiety in HCPs working in the province of Quebec (Canada) during the first wave of the COVID-19 pandemic and identify factors associated with changes in anxiety scores. METHODS: An exploratory online cross-sectional survey was conducted among Quebec HCPs from April to July 2020. The Spielberger's State-Trait Anxiety Inventory (STAI) was used to measure state anxiety among HCPs. Descriptive and multivariate analyses were performed. RESULTS: A total of 426 HCPs completed the survey. Anxiety scores ranged from 20 to 75 points, with 80 being the highest possible value on the STAI scale. Being a female HCP [B = 5.89, 95% confidence interval (CI): 2.49-9.3] and declaring having the intention to avoid caring for patients with COVID-19 (B = 3.75, 95% CI: 1.29-6.22) were associated with increased anxiety scores. Having more years of experience was associated with decreased anxiety scores [B = -0.2, 95% CI: -0.32-(-0.08)]. CONCLUSION: Organizational strategies aimed at preventing and relieving anxiety should target junior female HCPs who express the intention to avoid caring for patients with COVID-19. Seniority could become an important criterion in selecting frontline HCPs during pandemics. Further studies are needed to comprehensively examine the impacts of the COVID-19 pandemic on Canadian HCPs and identify evidence-based coping strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".