Relational and Intrapersonal Factors Driving Women’s Engagement in High-Risk Relationships
Bibliographic record
Abstract
Objective: This study aimed to explore the relational, intrapersonal, and sociocultural factors that contribute to women’s engagement in and persistence within high-risk intimate relationships. Methods and Materials: A qualitative research design was employed using semi-structured, in-depth interviews with 23 women residing in Mexico who had experienced at least one high-risk romantic relationship characterized by emotional harm, dependency, or coercive dynamics. Participants were recruited through purposive sampling, and data collection continued until theoretical saturation was reached. Interviews were transcribed verbatim and analyzed using thematic analysis supported by NVivo 14 software. Rigor was maintained through member checking, reflexive journaling, and peer debriefing. Findings: Three overarching themes emerged: (1) Emotional Vulnerability and Psychological Needs, including subthemes such as fear of loneliness, low self-worth, emotional dependency, and a need for validation; (2) Interpersonal Dynamics and Power Imbalance, highlighting manipulative control tactics, sexual coercion, and unequal decision-making power; and (3) Sociocultural and Structural Influences, encompassing traditional gender role expectations, economic dependence, stigma, and limited access to mental health support. Participants described enduring harmful relationships due to internalized relational schemas, structural constraints, and cultural narratives that framed endurance as moral strength. Emotional idealization of the partner and hope for redemption were also key drivers for continued engagement in high-risk dynamics. Conclusion: Understanding these multilayered factors is critical for designing culturally responsive, trauma-informed interventions that promote safety, autonomy, and healing. Interventions must address not only individual emotional needs but also systemic barriers to support and empowerment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".