Première vague de la COVID-19 au Québec : motivation du personnel soignant à traiter des patients infectés
Bibliographic record
Abstract
INTRODUCTION: Retention of healthcare workers (HCWs) in the healthcare system during the COVID-19 pandemic could become a challenge. It is therefore important to better understand what are the motivational elements that could explain a greater or lesser motivation to care for infected patients. OBJECTIVES: To evaluate factors modulating HCWs' willingness to treat COVID-19 infected patients. METHODS: HCWs from Québec, Canada, were invited to complete an online survey during the first wave of the COVID-19 pandemic between the months of April and July 2020. The survey focused on the intention to avoid treating infected patients, prior experiences in treating COVID-19 patients and anxiety levels. Descriptive statistics and multiple regression analysis were used to assess which factors explained differences in HCWs intention to avoid treating patients. RESULTS: A total of 430 HCW completed the survey. A majority were women (87%) and nurses (50%). Of those, 12% indicated having considered measures to avoid working with COVID-19 infected patients and 5% indicated having taken actions to avoid working with infected patients. A further 18% indicated that they would use a hypothetical opportunity to avoid working with infected patients. Having previously treated infected patients was associated with a significant reduction in the intention to avoid work (OR: 0.56 CI 0.36-0.86). Amongst HCWs, physicians had a significantly reduced intention to avoid treating infected patients (OR: 0.47 CI 0.23-0.94). We also found that an increase in anxiety score was associated with a greater intention to avoid treating COVID-19 infected patients (OR: 1.06 CI 1.04-1.08). CONCLUSION: Study results suggest that previous experience in treating COVID-19 infected patients is protective in terms of work-avoidance intentions. We also found that amongst HCWs, physicians had a significantly lower intention to avoid working with COVID-19 infected patients. Finally, our results show that increase in anxiety is associated with a higher intention to avoid treating infected patients. Characterization of factors associated with low anxiety levels and low reluctance to work during the COVID-19 pandemic could be useful in staffing facilities during the present and future healthcare crisis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".