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Record W2106770824 · doi:10.5430/jnep.v4n5p126

Menstrual disorders: The implications on health and academic activities of female undergraduates in a federal university in Nigeria

2014· article· en· W2106770824 on OpenAlexvenueno aff
Adekemi Eunice Olowokere, Monisola Omoyeni Oginni, Aanuoluwapo Omobolanle Olajubu, Augusta E. William, Omolola Irinoye

Bibliographic record

VenueJournal of Nursing Education and Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMenstruationAffect (linguistics)Descriptive statisticsMedicineAbsenteeismDescriptive researchFamily medicinePsychologyDemographyGynecologyInternal medicine

Abstract

fetched live from OpenAlex

Menstruation is a natural phenomenon in a female who has reached the age of puberty. However, it is often associated with some discomforts which may affect women’s health and academic activities of students. The study assessed the knowledge, management of menstrual disorders and the health and academic implications on young female under- graduates using a descriptive cross sectional design. A sample of 400 female undergraduates participated in the study. Data was collected using a 72-item semi structured questionnaire. Data collection lasted for two weeks and analysis was done using descriptive and inferential statistics at 0.05 level of significance. Result showed that 61% (n = 244) had good knowledge of menstrual disorders and its management. Most prevalent menstrual disorders found in the study was dysmenorrhoea. Missing school was the highest academic effect recorded (64.5%, n = 258) while Dizziness (51%, n = 204) was the highest health implication recorded. A significant association was found between dysmenorrhoea and school absenteeism (χ 2 = 65.7, P < .05). The study reiterated the need for early educational programme that will assist the female undergraduates to cope well with menstrual disorders without any effect on their health and academics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.083
GPT teacher head0.443
Teacher spread0.360 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

Explore more

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