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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Sep 11, 2024
Date Accepted: Apr 14, 2025

The final, peer-reviewed published version of this preprint can be found here:

Identifying Psychosocial, Self-Management, and Health Profiles Among Women With Chronic Pain Who Have Experienced Intimate Partner Violence and Those Who Have Not: Protocol for a 2-Phase Qualitative and Cross-Sectional Study Using AI Techniques

Nardi-Rodríguez A, Bernardes S, Pastor-Mira M, López-Roig S, Pamies Aubalat L, Sánchez-Prada A, Ferrer-Pérez VA, Rodríguez-Rodríguez I, Heras-González P

Identifying Psychosocial, Self-Management, and Health Profiles Among Women With Chronic Pain Who Have Experienced Intimate Partner Violence and Those Who Have Not: Protocol for a 2-Phase Qualitative and Cross-Sectional Study Using AI Techniques

JMIR Res Protoc 2025;14:e66396

DOI: 10.2196/66396

PMID: 40627849

PMCID: 12284455

Identification of psychosocial, self-management and health profiles among women with chronic pain with and without experience of intimate partner violence: Qualitative and cross-sectional study protocol with Artificial Intelligence

  • Ainara Nardi-Rodríguez; 
  • Sònia Bernardes; 
  • María Ángeles Pastor-Mira; 
  • Sofía López-Roig; 
  • Lidia Pamies Aubalat; 
  • Andrés Sánchez-Prada; 
  • Victoria A. Ferrer-Pérez; 
  • Ignacio Rodríguez-Rodríguez; 
  • Purificación Heras-González

ABSTRACT

Background:

Women victims of Intimate partner violence are up to 5 times more likely to develop disabling Chronic Pain (CP). Surprisingly, there is a lack of information on the meaning and implication of living with CP whist being victim of partner violence, and, despite the existence of well-established risk and protective factors for CP as well as health outcomes, there is no information on whether they differ in these women compared to those who have never experience violence before.

Objective:

Thus, our aims were, in comparison to women with CP only, to acquire knowledge on the meaning and implications of living with CP for victims of partner violence and to identify possible different profiles based on risk/protective factors and health outcomes.

Methods:

We designed two studies that will be conducted in two phases. In the first one, a qualitative study with a descriptive and exploratory design will be used. Individual semi-structured interviews will be conducted with at least 10 women with CP and other 10 women with CP who are victims of partner violence. For data analysis, reflexive thematic analysis will be performed, focusing on experiences, meanings and on the reality of participants. In the second phase, a cross-sectional study will be carried out with current and past victims of partner violence with CP and women with CP who have never experienced violence before. A total of 359 women victims and non-victims will have to respond to different scales on risk and protective factors (i.e. pain worrying, or activity patterns) and on health outcomes (i.e. legal and illegal drug consumption or use of healthcare settings). For data analysis, inferential statistics and machine learning will be used.

Results:

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Conclusions:

This research addresses two of the urgent need in pain research acknowledged by the European Pain Federation: 1) deepening knowledge on CP management based on contextual factors and 2) and, by possibly identifying specific psychosocial, self-management and health profiles among women with CP with and without experiences of partner violence, rise useful information to personalize pain management treatments. In addition, it could help identifying victims of partner violence in health settings.


 Citation

Please cite as:

Nardi-Rodríguez A, Bernardes S, Pastor-Mira M, López-Roig S, Pamies Aubalat L, Sánchez-Prada A, Ferrer-Pérez VA, Rodríguez-Rodríguez I, Heras-González P

Identifying Psychosocial, Self-Management, and Health Profiles Among Women With Chronic Pain Who Have Experienced Intimate Partner Violence and Those Who Have Not: Protocol for a 2-Phase Qualitative and Cross-Sectional Study Using AI Techniques

JMIR Res Protoc 2025;14:e66396

DOI: 10.2196/66396

PMID: 40627849

PMCID: 12284455

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