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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".