Interactional Pathology among Couples with Borderline Personality Disorder Wives: A Qualitative Study
Bibliographic record
Abstract
The purpose of this qualitative study was to gain a deeper understanding of interactional pathology among couples with the borderline personality female partners by examining the experiences of 10 men/male partners. This main inquiry question is/can be stated as follows; “How do men experienced their communication with their partner who has been diagnosed as displaying traits/characteristics?”. The latter was supplemented by various sub questions exploring the different dimensions of marital life. The major data collection tool was semi-structured interviews. Forty to 60-minute interviews were conducted with each participant. Data analysis included a three-phase process: description, reduction, and interpretation. The latter was completed using the qualitative content analysis by Colaizzi (1978).six categories of experiences indicating interactional difficulty among the couples were obtained from the data analysis. Marital communication pathology, interpersonal pathology, destructive cognition, destructive relations with children, relationship problems due to economical impulse control and social impulse control sense and descriptions may need attention. The results of inquiry determined that the root of interactional pathology can be ascribed to’ instability’. As regard the results it can be said the men with border line personality disorder wives endure interactional pathologies that finally they cause collapsing their family sentence needs attention.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".