Sexual behaviour research using the survey method: A critique of the literature over the last six years
Bibliographic record
Abstract
OBJECTIVES: The survey method is often used to identify trends in sexual behaviour and attitudes. In order for research conclusions to be valid, however, data gathered from surveys must be unambiguous. For the current paper we reviewed survey research of sexual behaviour published in recent years. Our objective was to identify common methodological issues and provide recommendations for how to address them. METHOD: We examined 62 articles, published in four prominent sexual health journals over the past six years. We evaluated each article based on how adequately its authors addressed four recurring methodological issues: ambiguous terminology, heterosexual bias, procedures that compromise honesty in responses, and survey language/literacy considerations. RESULTS: The review revealed some recurring issues: 47% of the studies failed to address the question of sexual orientation, sexual behaviour terminology was explicitly defined in only 32% of questionnaires, fewer than 5% of surveys clarified whether the sexual encounters in question were consensual, and 21% of the articles contained no mention of anonymity or confidentiality for participants. DISCUSSION: These results reveal common issues with survey data that compromise the validity of findings. In order to have confidence in research conclusions and recommendations, it is important that these issues be addressed.
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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 | 0.465 | 0.527 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".