The use of condoms to customers
Bibliographic record
Abstract
Data from the Health Department of Riau until early 2014 quarter there were 132 new cases of Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Pekanbaru tops the number of people with HIV and AIDS. The details of HIV cases and 76 56 AIDS cases cumulatively from 1997 until 2014 there were 2,388 cases of HIV and AIDS. The purpose of this research is to analyze the use of condoms to customers in the Jondul city of Pekanbaru by 2014. This research was qualitative research with the narrative approach gleaned from the results of the in-depth interviews on the main informant 4 persons ie customers and additional informants that "Mami" and sex workers. Data analysis was carried out with thematic analytical transcription, verification and the making of the matrix. The research showed that customers do not want the use of a condom during sexual intercourse to sex workers. Although sex workers have been trying to do deals to use condoms to customers but still no luck. The use of condoms to customers affected by perception, hassle and comfort in using those condoms. The City Health Office expects Soweto to conduct counseling regarding condom use, and required cooperation with local health professionals and NGOs in conducting coaching and training fitting condoms is good, correct and fast by sex workers to its customers.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".