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Record W4231735961 · doi:10.25311/keskom.vol2.iss4.71

Hubungan Tingkat Pengetahuan tentang Perubahan Fisiologis dan Psikologis Fase Premenopouse terhadap Tingkat Kecemasan pada Pralansia

2014· article· id· W4231735961 on OpenAlexaff
Zuliana, Ennimay, Yecy Anggreny

Bibliographic record

VenueJurnal kesehatan komunitas (Journal of community health) · 2014
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicGlobal Socioeconomic and Political Dynamics
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsGynecologyMedicine

Abstract

fetched live from OpenAlex

Knowledge was the resulted of human sensing that strongly influenced by the intensity of attention and object perception. Anxiety was a felt of fear that was not clear and not supported by the situation. This study aims to determine the relationship betwen level of knowledge about physiological and psychological changes in premenopausal phase and level of anxiety in pre-old woman. This research was quantitative design with cross-sectional. Total sample in this study are 90 premenopausal women who experience pre-old women at Working Area of Health Public Center in Pekanbaru, used purposive sampling technique. Data was analyzed with univariate and bivariate frequency distributions with chi-square test. The results showed an relationship between the level of knowledge about the physiological and psychological changes in premenopausal phase and the level of anxiety in pre-old women with p value = 0.000 (p <0.1). This study recommended that additional studies by increasing the number of samples so that the whole population can be represented.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.048
GPT teacher head0.284
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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