How attachment styles predict changes in sexual desire: A study of sexual dynamics in COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has had far-reaching impacts on many aspects of life, including sexual behaviours and preferences. In this longitudinal study, the authors used attachment theory to investigate changes in an individual’s sexual desire for their partner as well as changes in their sexual desire for someone other than their primary romantic partner (extradyadic desire) over the first wave of the pandemic in Canada. Based on past research that has shown that avoidant individuals tend to avoid intimacy, the authors reasoned that increased contact with their romantic partner due to physical distancing guidelines and lockdown rules would contribute to avoidant individuals’ experiencing less sexual desire for their partner and greater extradyadic desire over time. In contrast, individuals high on attachment anxiety tend to seek proximity, especially during times of stress. The authors predicted that individuals’ sexual desire for their partner would increase and their extradyadic desire would decrease. They tested these hypotheses using a cohabiting, dyadic sample ( N = 308 individuals); study participants were contacted at 1-month intervals for three successive months and asked to complete an online survey. Our hypotheses were partially supported. As predicted, individuals high on attachment avoidance experienced higher levels of extradyadic desire, and individuals high on attachment anxiety reported lower extradyadic desire over time. Contrary to predictions, however, neither attachment pattern was associated with changes in sexual desire for the partner. The authors examine the theoretical implications of these findings, highlighting the need for a more fine-grained assessment of stress and the interaction between stress and attachment orientations in future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".