Recognising and responding to intimate partner violence using telehealth: Practical guidance for nurses and midwives
Bibliographic record
Abstract
AIMS: To synthesise the current, global evidence-informed guidance that supports nurses and midwives to recognise and respond to intimate partner violence (IPV), and how these practices can be translated from face-to-face encounters to care that is delivered through telehealth. BACKGROUND: COVID-19-related social and physical distancing measures increase the risk for individuals who are socially isolated with partners who perpetuate violence. Providing support through telehealth is one strategy that can mitigate the pandemic of IPV, while helping patients and providers stay safe from COVID-19. DESIGN AND METHODS: In this discursive paper, we describe how practical guidance for safely recognising and responding to IPV in telehealth encounters was developed. The ADAPT-ITT (Assessment, Decisions, Administration, Production, Topical Experts, Integration, Testing, Training) framework was used to guide the novel identification and adaptation of evidence-informed guidance. We focused on the first six stages of the ADAPT-ITT framework. CONCLUSIONS: This paper fills a gap in available guidance, specifically for IPV recognition and response via telehealth. We present strategies for prioritising safety and promoting privacy while initiating, managing or terminating a telehealth encounter with patients who may be at risk for or experiencing IPV. Strategies for assessment, planning and intervention are also summarised. System-level responses, such as increasing equitable access to telecommunication technology, are also discussed. RELEVANCE TO CLINICAL PRACTICE: Integrating innovative IPV-focused practices into telehealth care is an important opportunity for nurses and midwives during the current global COVID-19 pandemic. There are also implications for future secondary outbreaks, natural disasters or other physically isolating events, for improving healthcare efficiency, and for addressing the needs of vulnerable populations with limited access to health care.
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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 | 0.064 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".