Level of patient contact and Impact of Event scores among Canadian healthcare providers during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Healthcare providers (HCP) continue to provide patient care during the COVID-19 pandemic despite the known risks for transmission. Studies conducted early in the pandemic showed that factors associated with higher levels of distress among HCP included being of younger age, female, in close contact with people with COVID-19, and lower levels of education. The goal of this study was to determine if level of patient contact was associated with concern for post-traumatic stress disorder (PTSD) as measured by the Impact of Event Scale-Revised (IES-R). METHODS: This cross-sectional study, embedded within a prospective cohort study, recruited HCP working in hospitals in four Canadian provinces from June 2020 to June 2023. Data were collected at enrolment and annually from baseline surveys with the IES-R scale completed at withdrawal/study completion. Modified Poisson regression was used to determine the association between level of patient contact and concern for PTSD (i.e., IES-R scores ≥24). RESULTS: The adjusted rate ratio (RR) associated with concern for PTSD among HCP with physical contact/direct patient care was 1.19 (95% confidence interval (CI) 1.03, 1.38) times higher than for HCP with no direct contact. In fully adjusted linear regression models, physical care/contact was associated with higher avoidance and hyperarousal scores, but not intrusion scores. CONCLUSIONS: Administrators and planners need to consider the impact of heightened and ongoing stress among HCP by providing early screening for adverse emotional outcomes and delivery of tailored preventive strategies to ensure immediate and long-term HCP health.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".