Factors Contributing to Increased Workplace Violence Against Nurses During COVID-19 in the Healthcare Settings of a Lower Middle-income Country: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: The aim of this study was to provide the perceptions of nurses, nursing supervisors, and nursing administrators about factors contributing to increased workplace violence (WPV) against nurses within the healthcare settings in Pakistan during the first wave of the COVID-19 pandemic. METHODS: This study used a Descriptive Qualitative design, with a purposive sampling technique. From September to December 2021, In-depth interviews of 45 to 60 minutes, using a semistructured interview guide, we collected data from a private and a public healthcare setting in Pakistan. Given the travel restrictions during the COVID-19 pandemic, these interviews were conducted online, using Zoom audio features. Bedside nurses, nursing supervisors, and nursing administrators with at least six months of work experience participated in this study. RESULTS: The qualitative data analysis steps suggested by Braun and Clarke (2013) were used for thematic analysis. The overarching theme emerging from the data was "Factors perceived by nurses that contributed to increased WPV in their work settings during the first wave of COVID-19, in a lower middle-income country" The subthemes from the participants' narrations were (a) highly stressed patients, attendants, and healthcare workers; (b) the financial burden on patients and their families; (c) lack of resources and shortage of staff; (d) restricted visiting policy and a weak security system; (e) lack of awareness about the seriousness of COVID-19; (f) misconceptions about COVID-19 vaccines and nurses' role in disseminating awareness. CONCLUSIONS: The current pandemic increased the intensity of WPV against nurses in healthcare settings in Pakistan. Despite any supposed reasons for WPV, exposure to violence should never be an acceptable part of nursing. The healthcare system in Pakistan needs to pay equal attention to funding, resource provision, and ensuring a safe working environment for healthcare workers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".