Peering behind the mask: The roles of reactance and gender in the relationship between self‐esteem and interpersonal problems
Bibliographic record
Abstract
OBJECTIVE: When a client feels a threat to their freedom or autonomy as a result of external feedback, they can act out and respond in maladaptive ways. This state-referred to as reactance-has potential ramifications on interpersonal functioning. However, the underlying factors exacerbating this response including self-esteem and gender are yet to be extensively explored in a clinical sample. The present study examined whether verbal and/or behavioural reactance mediate the relationship between self-esteem and interpersonal problems and if this mediational relationship differs between men and women. METHOD: Patients with personality dysfunction (N = 136) completed pretreatment assessments of reactance, self-esteem, and interpersonal problems, and a conditional process model using these constructs was tested. RESULTS: Findings indicated that the moderated mediation model was significant, pointing to behavioural reactance as a significant mediator in the association between self-esteem and interpersonal problems. Furthermore, the findings revealed that gender moderated the relationship between self-esteem and behavioural reactance, indicating that this association may apply specifically to men low in self-esteem. DISCUSSION: These results shed light on how behavioural reactance may be an important manifestation of low self-esteem for men and a key contributor to their interpersonal problems. The findings draw attention to the importance of considering different factors at play when working with reactant individuals in therapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".