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Record W4409677852 · doi:10.52711/2454-2652.2025.00003

A Descriptive Study to Assess the Knowledge regarding prevention of Urinary Tract Infection among adolescent girls in Nanchiyampalayam, Dharapuram, Tamilnadu

2025· article· en· W4409677852 on OpenAlexaff
Pauline Sharmila, D. Shobiya, A. Gunavathi, D. Shalini, Jennie Rose

Bibliographic record

VenueInternational Journal of Advances in Nursing Management · 2025
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsBishop's University
Fundersnot available
KeywordsUrinary systemMedicineFamily medicineEnvironmental healthTraditional medicineInternal medicine

Abstract

fetched live from OpenAlex

A descriptive study to assess the knowledge regarding prevention of urinary tract infections among adolescent girls in Nanchiyampalayam, Dharapuram, Tamilnadu. The non-experimental research approach was adopted for the study with descriptive research. The sample size was 30. Sample of adolescent girls who met the inclusion criteria were selected for the study by using purposive sampling technique. Structured questionnaire was used to assess the knowledge regarding urinary tract infection. The data were analysed by using both descriptive and inferential statistics. Regarding age, majority 16(53%) belongs to the age group of 15-16 years. Regarding religion, majority 13(44%) of them were Hindus. Regarding Occupation of the father, highest of 11(36%) were coolie workers. Regarding family income per month, majority were belonged to the income group of Rs.5001 and above 14(47%). Regarding education, majority 10(33%) had completed their primary education. Regarding source of information, majority 15(50%) through Health personnel. Regarding the level of knowledge, majority 16(53%) had adequate knowledge. There was significant association between the level of knowledge regarding urinary tract infection with their selected demographic variables among adolescent girls such as age at 0.05 level of significance.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.181
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.393
Teacher spread0.368 · 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
Published2025
Admission routes1
Has abstractyes

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