Bibliographic record
Abstract
Problem/Issue and significance: Intimate partner violence [IPV] is a critically urgent public health issue worldwide. Violence and trauma have multiple short- and long-term health effects including pain, which is a complex, highly subjective experience that can have profoundly negative impacts on both “body and soul.” Untreated pain can become chronic and debilitating, leading to a lower quality of life, decreased employment opportunities, and increased mental health concerns. Addressing pain is an important aspect of the health care professional’s role, and the failure to do so can exacerbate existing health disparities and worsen health outcomes. Current approaches to understanding and managing pain are primarily based on a Westernized view of health and illness. Diversity in knowledge and perspectives is missing from clinical practice, nursing education, and research, and is needed to improve pain assessment and management practices. Approach or innovation: While health care practitioners recognize that more holistic approaches to understanding, assessing, and treating pain are required, the significance of women’s chronic pain experiences in the context of IPV has been largely unexplored. This paper proposes the creation of “pain profiles” using an approach that draws of multiple sources of information and types of knowledge to gain new insights and to develop a richer understanding of women’s pain experiences. The unique circumstances and experiences of Indigenous women are used to illustrate the usefulness of the pain profiles as a case in point. In Canada, Indigenous women experience higher levels of violence and trauma as a result of interpersonal violence, historical trauma, and ongoing socio-economic inequities and systemic racism than non-Indigenous women. When Indigenous women seek health care for pain, they often report a lack of culturally safe and appropriate services and are often labeled as “drug-seeking” which can cause further delays or avoiding seeking care. Few existing health services address pain arising from violence and trauma for women, especially services that focus on cultural safety and trauma-and-violence informed care. Lessons and implications: Pain profiles provide a novel and more nuanced approach to understanding, and addressing, pain in women who have experienced violence by using a holistic approach that draws on various perspectives and multiple sources of information, including the women’s own words. This approach may help to guide the implementation of more culturally safe, trauma-and-violence informed health practices and services, strengthen nursing curriculum around pain assessment and management, and help improve the overall well-being and quality of life of all women who have experienced violence. [An updated file with minor revisions was uploaded on 2026-07-03; the Open Collections Usage Statistics were manually modified in 2026-07].
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".