The psychological impact of the COVID-19 pandemic and a SARS-CoV-2 testing programme on obstetric patients and healthcare workers
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to describe the psychological impact of the COVID-19 pandemic and the specific impact of a universal SARS-CoV-2 testing programme on obstetric patients and healthcare workers at The Ottawa Hospital. METHODS: This was a follow-up survey study of obstetric healthcare workers and then-pregnant patients who participated in a SARS-CoV-2 testing programme conducted in The Ottawa Hospital obstetrical triage units from 19 October to 17 November 2020. Surveys explored the effects of the COVID-19 pandemic and the testing programme on participants' psychological well-being. Responses were collected from April to September 2021. Descriptive summary statistics were calculated for both groups. RESULTS: During hospitalization for delivery, obstetric patients (n = 143) worried about giving COVID-19 to their new baby (88.11%), catching COVID-19 (83.22%), and giving COVID-19 to their partner (76.22%). Patients felt relief at being tested for COVID-19 during the universal testing programme (24.65%) and at getting their results (28.87%). Patients also believed that universal SARS-CoV-2 testing was a good way to slow COVID-19 spread (79.72%), reduce anxiety (75.52%), and increase relief (76.22%). In addition, patients felt good about participating in research that could help others (91.61%). Among obstetric healthcare workers (n = 94), job satisfaction decreased and job stress increased during the COVID-19 pandemic. The universal testing programme led to minor increases in healthcare worker job stress and burden, particularly among nurses, but the majority (85.23%) believed it was a valuable research initiative. CONCLUSION: The COVID-19 pandemic has had a negative psychological impact on obstetric patients and healthcare workers. Universal SARS-CoV-2 testing was generally viewed favourably and may serve as an effective strategy for estimating COVID-19 prevalence without adding undue stress onto patients and healthcare workers during the pandemic.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".