Autonomy support in disclosure and privacy maintenance regulation within romantic relationships
Bibliographic record
Abstract
Abstract Romantic partners vary in their desire to share private information, and each partner must use appropriate strategies to elicit disclosure or maintain privacy from one's partner. In the present work, we propose that strategies that support the partner's autonomy, rather than being controlling, may be more acceptable and effective in eliciting disclosure and maintaining privacy in romantic relationships. In Study 1 (N = 268 individuals), participants rated the acceptability and effectiveness of autonomy supportive and controlling strategies presented in hypothetical scenarios. In Study 2 (N = 78 couples), we coded romantic partners' use of autonomy supportive and controlling strategies in recorded conversations, then assessed the acceptability and effectiveness of strategies. In both studies, autonomy supportive strategies were perceived as more acceptable and more effective than controlling strategies for eliciting disclosure and maintaining privacy from one's partner. Additionally, results of Study 2 demonstrated that eliciting disclosure using autonomy supportive strategies rather than controlling strategies resulted in greater and more personal content in partner disclosure. The results are discussed with reference to couples' interventions and the potential of autonomy supportive strategies to improve the quality of couples' communication and relationship quality.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".