Differences in evoked emotions, feelings and reactions to body and mouth odour
Bibliographic record
Abstract
Objective: To compare evoked emotions, feelings and reactions to body and mouth odour among undergraduates.Methods: This questionnaire-based cross-sectional study was conducted among undergraduates of the University of Benin, Benin City, Nigeria.Results: Nearly one-quarter of the participants reported taking into account body odour (23.3%) and mouth odour (24.7%) on meeting people on often/always basis. About half of the participants stated being very disgusted on perception of body odour (52.7%) and mouth odour (52.0%). About one-quarter (24.0%) of the participants expressed anger when in contact with someone with body or mouth odour. About two-thirds (64.5%) and 76.0% of the participants reported being slightly/very unhappy having a classmate/roomate with body or mouth odour respectively. About 12.0% and 12.7% agreed that students with body and mouth odour respectively should be expelled from the university. Assessing reactions to someone with body or mouth odour in a commercial vehicle; 13.3% versus 10.7% changed position and 7.3% versus 8.7% dropped off the vehicle respectively. The majority of the participants felt that body odour or mouth odour negatively influence good employment potential, marriageability and marital relationship but there was no difference. Low proportion of the participants reported avoidance behaviour as their preferred way to help someone with body (10.0%) or mouth (9.3%) odour.Conclusion: Data from this study revealed no differences in the evoked emotions, feelings, perceptions and reactions toward body and mouth odour sufferers among the participants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".