Postpartum nurses' perceptions of barriers to screening for intimate partner violence: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a human rights violation that is pervasive worldwide, and is particularly critical for women during the reproductive period. IPV includes physical, sexual and emotional abuse. Nurses on in-patient postpartum units are well-positioned to screen women for IPV, yet low screening rates suggest that barriers to screening exist. The purpose of this study was to (a) identify the frequency of screening for IPV, (b) the most important barriers to screening, (c) the relationship between the barriers to screening and the frequency of screening for types of abuse, and (d) to identify other factors that contribute to the frequency of screening for IPV. METHODS: In 2008, we conducted a cross-sectional survey of 96 nurses from postpartum inpatient units in three Canadian urban hospitals. The survey included the Barriers to Abuse Assessment Tool (BAAT), adapted for postpartum nurses (PPN). Ordinary least squares (OLS) regression models were used to predict barriers to screening for each type of IPV. RESULTS: The frequency of screening varied by the type of abuse with highest screening rates found for physical and emotional abuse. According to the BAAT-PPN, lack of knowledge was the most important barrier to screening. The BAAT-PPN total score was negatively correlated with screening for physical, sexual, and emotional abuse. Using OLS regression models and after controlling for demographic characteristics, the BAAT-PPN explained 14%, 12%, and 11% of the variance in screening for physical, sexual and emotional abuse, respectively. Fluency in the language of the patient was negatively correlated with screening for each type of abuse. When added as Step 3 to OLS regression models, language fluency was associated with an additional decrease in the likelihood of screening for physical (beta coefficient = -.38, P < .001), sexual (beta coefficient = -.24, P = .05), and emotional abuse (beta coefficient = -.48, P < .001) and increased the variance explained by the model to 25%, 17%, and 31%, respectively. CONCLUSIONS: Our findings support an inverse relationship between rates of screening for IPV and nurses' perceptions of barriers. Barriers to screening for IPV, particularly related to knowledge and language fluency, need to be addressed to increase rates of screening on postpartum units.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".