Demand‐avoid‐withdraw processes in adolescent dating aggression
Bibliographic record
Abstract
We conducted an observational study of a collection of interactive processes known as "demand-withdraw" in relation to adolescent dating aggression. Couples (N = 209) aged 14-18 years participated in a challenging observational laboratory assessment to measure demands (i.e., pressures for a change), as well as demand → partner withdraw and demand → partner avoid sequences. Actor and partner effects were disentangled via dyadic data analyses. The results indicated a fairly consistent pattern in which demand → withdraw and demand → avoid sequences led by either partner were positively associated with both partners' physical and psychological aggression (measured via a dual informant questionnaire method). Further, higher quality demands (i.e., pressures for change that were specific and encouraged both members of the dyad to increase a given behavior) were inversely associated with aggression. Yet, all of the above associations were attenuated to the point of statistical nonsignificance after controlling for hostility. These results suggest two primary possibilities. The associations of demand → withdraw and demand → avoid sequences with dating aggression may be spurious, with the sequences merely markers for hostility, a known correlate of dating aggression. Alternatively, hostility may mediate the relations of demand → withdraw and demand → avoid sequences with dating aggression. Further research is required to test these competing explanations. Implications for preventive intervention are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".