Perceived barriers and rewards to sexual consent communication: A qualitative analysis
Bibliographic record
Abstract
Increasingly, affirmative consent - direct, unambiguous and voluntary agreement to engage in sexual activity (Craig & McKinley, 2015) - is the standard being adopted by educational institutions in North America (Bennett, 2016). Yet, studies show that most individuals continue to communicate consent through nonresistance (Jozkowski et al., 2014a). Given this discrepancy, it is critical to understand what factors prevent individuals from engaging in affirmative consent. Furthermore, a better understanding of the perceived rewards of consent communication could incentivize the adoption of affirmative consent. To understand the range of perceived barriers and rewards, we conducted an online, qualitative study where 231 participants answered two open-ended questions. We used inductive content analysis to categorize participants' perceptions of sexual consent barriers and rewards into four general content areas: (1) Communication Quality, (2) Relational and Emotional Experiences, (3) Sexual Quality and (4) Safety and Coercion. These perceived rewards and barriers were examined through the lens of the Information-Motivation-Behavioural Skills Model. Participants viewed consent communication not only as a means of ensuring safety but also as a way to enhance relational and sexual quality. However, they also perceived barriers in all three of these domains as well as barriers to ensuring that sexual consent communication is fluid and easily understood. These findings provide important avenues for future research investigating how individuals reconcile perceived rewards and costs of affirmative consent communication. We also suggest ways to enhance sexual education by discussing potential rewards and validating the normative nature of fears and anxieties around affirmative consent.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".