“A STUDY TO ASSESS EFFECTIVENESS OF PLANNED TEACHING PROGAMME ONPREVENTION OF CERVICAL CANCER IN TERM OF KNOWLEDGE AMONG FINAL YEAR COLLEGE STUDENTS OF SURAT CITY.”
Bibliographic record
Abstract
Cervical cancer is a major cause of cancer mortality in women and more than a quarter of its global burden is contributed by developing countries. In India, cervical cancer contributes to approximately 6–29% of all cancers in women. Cervical cancer ranks as the 1st most frequent cancer among women in India, and the 1st most frequent cancer among women between 18 and 44 years of age. So, the knowledge for prevention of cervical cancer is very vital part. A study to assess effectiveness of PTP on prevention of Cervical cancer in terms of knowledge among final year college students of Surat city. The objectives were to assess the knowledge of final year college students before and after the administration of PTP. Pre experimental research approach used with one group pre- test and post- test design. The investigator used multi stage random Sampling technique for selecting the 30 samples. Planned teaching programme on prevention of cervical cancer was prepared for the samples. A structured knowledge questionnaire was prepared to assess the knowledge of the samples. Descriptive and inferential statistics were used to analyse the data. The mean pre - test knowledge score was 13.25 and the mean post -test knowledge score was 16.38 with 3.13 significant mean difference. Hence it was concluded that PTP was effective in improving the knowledge of final year college students of Surat city.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".