Bibliographic record
Abstract
The ejaculatory behavior of 260 men was explored using eight different criteria for assessing premature or rapid ejaculation (RE). Estimates of the prevalence of RE were found to be sensitive to variations in RE operationalization, with very few to more than half of the men in the current sample classified as having RE depending upon which of several RE operationalizations were used. Twenty‐three percent of the men identified themselves as having an RE problem. The various RE criteria were only moderately intercorrelated, indicating that they measure separate aspects of RE. Discriminant function analysis identified three components to men's self‐identification as having a current RE problem: a behavioral component, an affective component, and an efficacy component. Only frequency of intercourse, and not age, sexual experience, rushed early intercourse experiences, or negative early intercourse experiences, was associated with having a self‐identified RE problem. Assessment and etiological issues related to RE are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".