Impact of the COVID-19 pandemic on the experiences of hepatology nurses in Canada
Bibliographic record
Abstract
Background: In March 2020, COVID-19 was declared a global pandemic, directly affecting the management of liver disease. Aims: This study aimed to gain insights on the impact of COVID-19 on Canadian hepatology nursing care practices, on the personal stress levels of nurses and on strategies employed in the delivery of care. Methods: The 129 members of the Canadian Association of Hepatology Nurses (CAHN) were invited to an online survey, with a mixed-methods design consisting of 22 quantitative and seven optional qualitative questions. Findings: Of CAHN members, 41 (32%) responded to the survey; 90% reported moderate-to-severe negative impacts on practice settings, while 68% reported hepatitis C testing and treatment delays. The qualitative data identified six main themes within two broad categories: barriers in access to care and strategies employed by nurses. Conclusions: Participants identified that COVID-19 had negative impacts on themselves personally and on their delivery of healthcare to patients. Hepatology nurses led positive changes through collaboration with community partners and mobilisation of outreach work.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".