The Influence of Conversational Content on College Students’ Safe Sex Intentions: A Mixed Method Approach
Bibliographic record
Abstract
Even though health campaign designers are advised to specifically focus on triggering conversations between people about health issues, there is still a lot unknown about what aspects of a conversation may contribute to safe sex behavior and intentions. Empirical research in this field so far has mainly focused on conversational occurrence rather than conversational content, and where content is taken into account, this mostly concerns self-reports. In this mixed method study, we looked into the quantitative effects of real-life conversations about safe sex, triggered by a safe sex message, on college students’ intentions related to safe sex. We then used a qualitative analysis to try and identify content-related aspects that may be related to the quantitative effects. Two weeks after filling in a questionnaire on their safe sex-related intentions, participants (N = 24) were instructed to watch and talk about a safe sex video with a conversation partner of choice, followed by filling in a questionnaire. The conversational data were analyzed qualitatively. The results suggest that the conversations increased safe sex-related intentions compared to pretest scores, and that content-related aspects such as conversational valence, type of communication behavior and behavioral determinants were related to these effects. Thus, our findings provide enhanced insight into the social norms and behavioral patterns related to safe sex, and indicate that it is important to look at conversational content in detail rather than to focus on mere conversational occurrence or quantitative effects.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".