Relationship Patterns in Alexithymia: A Study Using the Core Conflictual Relationship Theme Method
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a disturbance in regulating affective states. Clinical observations suggest that alexithymic patients relate to others in a specific way. This paper explores whether specific relationship or transference patterns are typical of alexithymia. SAMPLING AND METHODS: Relationship patterns were assessed by means of the Core Conflictual Relationship Theme method, standard categories version. This method examines transference patterns and was applied to clinical interview data collected from a sample (n = 31) of mental health outpatients. Alexithymia was assessed by means of a score on the 20-item Toronto Alexithymia Scale corrected for the degree of depression (measured by the Beck Depression Inventory-II). Data were analysed by means of the leaps and bounds regression algorithm for selecting optimal subsets of indicators and by bootstrapping to determine 95% confidence intervals. RESULTS: First, we observed that alexithymia can be meaningfully explained by typical wishes, typical subjective perceptions of how others respond and one's own typical responses to others. This result indicates that the more marked a patient's alexithymic traits are, the more probable it is that specific transference themes come to the fore. Second, a set of three core indicators of trait alexithymia was mapped: little concern about being good to others, a strong perception of others as cooperative, and weak levels of reacting to others and to conflict by means of somatic symptoms. CONCLUSION: Alexithymia is related to a double interpersonal indifference: not much is expected from others, nor is there a personal urge to fulfill the expectations of others. Moreover, in alexithymia somatic symptoms proved to be non-reactive to interpersonal situations. Implications for diagnosis and treatment are highlighted. Limitations of our study are that alexithymia was only assessed with a self-report measure and that conclusions are based only upon data from a heterogeneous mental health sample.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".