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Record W4322501849 · doi:10.36106/paripex/6902461

“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.”

2023· article· en· W4322501849 on OpenAlexaboutno aff
Swati J. Gamit, Jimmyjames J. Mogaria

Bibliographic record

VenuePARIPEX INDIAN JOURNAL OF RESEARCH · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsCervical cancerMedicineTest (biology)CancerFamily medicineQuarter (Canadian coin)Cancer preventionDemographyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.166
GPT teacher head0.516
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePARIPEX INDIAN JOURNAL OF RESEARCHSame topicCervical Cancer and HPV ResearchFrench-language works237,207