Assetjaments, abusos, agressions sexuals en estudiants d'infermeria en context de festa i sota el consum de tòxics: treball d'investigació
Bibliographic record
Abstract
Introduction: Sexual harassments, abuses and assaults are acts included \nwithin the concept of sexual violence. This is the most frequent form of gender \nviolence. Moreover, these acts are mainly originated by men and against \nwomen. The incidence of gender violence is difficult to estimate due to the fact \nthere are very few reported cases. Countries such as EEUU and Canada \ncarried out various studies about sexual abuses and sexual assaults in college \ncampuses. The results show that the prevalence is high and these acts are \nrelated to drug and alcohol consumption and the party assistance. Concerning \nSpain and Catalonia, there is few information about sexual harassments, \nabuses and assaults in college students. \nObjective: Identify the relation among sexual harassments, abuses and \nassaults in nursing students connected to drug and alcohol consumption in \nparties. \nMaterial and methods: The population studied is nursing students of Girona \nUniversity enrolled in year 2016-2017. The sample is about 304 students. The \nstudy is a transversal quantitative study using an ad-hoc survey and the Sexual \nExperience Survey Short Form Victimization (SES-SFV.2007) survey. The both \nsurveys are being prepared through Machform program to distribute it \nelectronically. \nResults: 81,2% of nursing students affirm to consume alcoholic drinks in \nparties and 7,3% confirm to use drugs. In this study, there have been reported \n43 sexual abuses, 9 sexual assaults and the 92,3% of the girls affirm to have \nsuffered sexual harassments. The 45,09% of all these acts have been produced \nin party context
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".