Edukasi Upaya Deteksi Dini Kanker Serviks di Wilayah Kerja Puskesmas Tahtul Yaman Kota Jambi
Bibliographic record
Abstract
Community preparation in carrying out early detection of cervical cancer such as VIA examination in the form of community mobilization (Empowerment) is needed so that awareness, knowledge and ability of IVA examination targets in carrying out VIA examinations increase according to the Ministry of Health in 2015. With this improvement, it is expected to increase the coverage of VIA examinations at the puskesmas, especially in this case at the Tahtul Yaman Health Center. Community service is carried out through socialization in the form of counseling on early detection of cervical cancer and educational training activities for cervical cancer early detection. The results of the socialization of cervical cancer early detection and training on cervical cancer early detection education showed that there was an increase in the knowledge of participants who took part in the activity compared to before and after participating in the training, from 78.33 to 85.56. Statistical results obtained p value = 0.002, meaning that there is a significant difference in the average test scores before being given training and after being given training. It is hoped that there will be the formation of Educational Cadres for Early Detection of Cervical Cancer in the Work Area of the Tahtul Yaman Health Center in Jambi City and the provision of pocket books that are easily understood by the public.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".