Alexithymia and Relationship Dissatisfaction: The Role of Conflict Communication Patterns
Bibliographic record
Abstract
Objective: To test a structural model in which alexithymia predicts relationship dissatisfaction both directly and indirectly through destructive conflict communication patterns among Malaysian adults. Methods and Materials: A descriptive correlational design was used with 423 adults in ongoing romantic relationships recruited across Malaysian states. Measures included the Toronto Alexithymia Scale–20 (TAS-20), the Communication Patterns Questionnaire (CPQ), and the Couples Satisfaction Index (CSI; reverse-scored to indicate dissatisfaction). Data screening confirmed statistical assumptions before analyses. IBM SPSS 27 was used for descriptive statistics and Pearson correlations, and AMOS 21 for structural equation modeling (SEM) with maximum likelihood estimation. Model fit was evaluated using χ², χ²/df, GFI, AGFI, CFI, TLI, and RMSEA; mediation was tested via indirect paths. Findings: Alexithymia correlated positively with destructive conflict communication (r = .56, p < .001) and relationship dissatisfaction (r = .48, p < .001); conflict communication correlated most strongly with dissatisfaction (r = .62, p < .001). The SEM demonstrated good fit: χ²(132) = 287.43, χ²/df = 2.18, GFI = .93, AGFI = .91, CFI = .96, TLI = .95, RMSEA = .053. Direct paths were significant from alexithymia → conflict communication (b = 1.12, β = .56, p < .001), alexithymia → dissatisfaction (b = 0.48, β = .29, p < .001), and conflict communication → dissatisfaction (b = 0.53, β = .48, p < .001). The indirect effect of alexithymia on dissatisfaction via conflict communication was significant (b = 0.59, β = .27, p < .001), yielding a total effect of β = .56 (p < .001), consistent with partial mediation. Conclusion: Alexithymia is a robust interpersonal risk factor for relationship dissatisfaction, and its impact is partly transmitted through destructive conflict communication. Findings highlight dual intervention targets—enhancing emotional awareness and improving conflict-management skills—to mitigate dissatisfaction in intimate relationships within a multicultural Malaysian context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".