Intimate Partner Violence in COVID-19: A Literature Review
Bibliographic record
Abstract
Abstract The silent pandemic that rages simultaneously behind the scenes of the COVID-19 is intimate partner violence (IPV). Intimate partner violence occurs when one partner uses abusive behavior to control or harm the other partner in the relationship. Due to public health orders including the stay-at-home initiated in response to the pandemic from March 2020 IPV incidents have increased. Purpose: The purpose of this study is to review the current literature that evaluates the impact that the COVID-19 public health orders have had on the IPV victims during the pandemic. Research Question:How have IPV victims been impacted by the COVID-19 pandemic? Method: A targeted literature review using PICO format (population, intervention, comparison, and outcomes) examines how IPV victims have been impacted by the COVID-19 pandemic and factors associated with the increased rates of IPV. Results: Comparisons of pre-COVID-19 IPV rates to the rates of IPV during the pandemic reveal outcomes an elevated number of IPV numbers during the pandemic, particularly with the abuse that is more severe. Risk factors for the increased rate of IPV included financial factors, care giver burnout, stress and other factors are discussed. Implications: Health care professionals have a key role to play in helping IPV victims to access resources Key words: COVID-19, novel corona virus, intimate partner violence, domestic violence, nursing
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".