An EFA of a revised Condom Fit and Feel Scale (CoFFee-R): Adding intimacy and pleasure
Bibliographic record
Abstract
External condoms are an effective contraceptive and safe-sex practice when used properly. A focus on universal fit has produced a variety of latex, polyurethane, or polyisoprene condoms. However, researchers and manufacturers have given little attention to how condoms subjectively fit and feel for the user. Reece and colleagues (2007) developed the Condom Fit and Feel Scale in 2007 that focused on physical aspects of fit (e.g., loose along penis shaft). Yet, users’ perceptions of emotional intimacy and pleasurable sensations may also predict condom use. The original scale had items corresponding to one of five factors: Condoms Fit Fine, Condoms Feel Too Loose, Condoms Feel Too Tight, Condoms Are Too Long, and Condoms Are Too Short. We revised the measure to include a broader conceptualization of feel and to remove redundant items. This revised scale (CoFFee-R) was tested for its psychometric properties in a sample of 399 participants recruited through a university participant pool. After conducting parallel and exploratory factor analyses, we settled on a four-factor structure. Our resulting CoFFee-R scale contained 18 items with the following factors: Condom Fit, Condom Intimacy and Pleasure, Condom Big, Condom Small. The factor structure of the CoFFee-R accounted for 57.6% of the variance, with structure matrix loadings ranging from .54 to .99, and no cross-loadings above .33. We discuss benefits and uses of the CoFFee-R within a transtheoretical model of predicting condom-use. This study has implications for how researchers measure health behaviours, from the wording of items to the conceptualization of condom fit and feel.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".