Rejection Sensitivity and Attachment Avoidance as Predictors of Intimacy Struggles in Romantic Relationships
Bibliographic record
Abstract
Objective: This study aimed to investigate whether rejection sensitivity and attachment avoidance significantly predict intimacy struggles in romantic relationships. Methods and Materials: A correlational descriptive research design was employed using a sample of 433 adults from Mexico, selected based on the Morgan and Krejcie sample size table. Participants completed standardized self-report measures assessing rejection sensitivity, attachment avoidance, and intimacy struggles. Data were analyzed using SPSS-27, including Pearson correlation to examine the relationships between the dependent variable and each independent variable, and multiple linear regression to assess the predictive power of the independent variables on intimacy struggles. All statistical assumptions were checked and met before performing the analyses. Findings: Results indicated significant positive correlations between intimacy struggles and both rejection sensitivity (r = .51, p < .01) and attachment avoidance (r = .46, p < .01). Multiple regression analysis revealed that rejection sensitivity (β = .39, t = 7.75, p < .01) and attachment avoidance (β = .33, t = 7.31, p < .01) were both significant predictors of intimacy struggles. The overall model was statistically significant, F(2, 430) = 105.89, p < .01, with an R² of .33, indicating that 33% of the variance in intimacy struggles was explained by the predictor variables. Conclusion: The findings highlight that both rejection sensitivity and attachment avoidance are key psychological factors contributing to intimacy struggles in romantic relationships. These results underscore the importance of addressing underlying emotional vulnerabilities in relationship counseling and psychological interventions, particularly in culturally diverse populations such as adults in Mexico.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".