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Record W4389790133 · doi:10.54832/judimas.v1i2.163

Peningkatan Edukasi Stroke Masyarakat dengan Video Edukasi Stroke – “CHERIE” (Cerebral Hemorrhage and Ischemic Educational Video)

2023· article· id· W4389790133 on OpenAlexaff
Firdha Aprillia Wardhani, Amalia Wardatul Firdaus, Shinta Mayasari

Bibliographic record

VenueJurnal Pengabdian Masyarakat (JUDIMAS) · 2023
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsStroke (engine)MedicinePhysics

Abstract

fetched live from OpenAlex

Berdasarkan data Kementerian Kesehatan, stroke merupakan penyebab kematian pertama untuk semua umur dengan persentase 15,4 persen, dan stroke juga menempati urutan pertama penyakit tidak menular yang mematikan. Time window yang ditujukan untuk pengobatan stroke adalah tiga jam, sehingga pengenalan gejala stroke sedini mungkin kepada masyarakat sangat penting. Tujuan Pengabdian masyarakat ini adalah untuk mengedukasi masyarakat dengan video edukasi stroke CHERIE (Cerebral Hemorrhage and Ischemic Educational Video) bagi masyarakat untuk lebih memahami penyakit stroke, mulai dari gejala hingga petunjuk penggunaan obat. Sasaran kegiatan ini adalah masyarakat umum di wilayah kabupaten Jember. Metode yang digunakan adalah memberikan edukasi dan mengukur tingkat pengetahuan melalui pretest dan post-test untuk mengetahui perubahan tingkat pengetahuan. Hasil dari kegiatan ini, sasaran mengalami peningkatan pengetahuan dengan rata-rata 42,8%, dengan nilai rata-rata pretest sebesar 68, dan rata-rata nilai post-test sebesar 95,3. Sehingga dapat disimpulkan bahwa kegiatan Pengabdian masyarakat dalam Upaya peningkatan edukasi masyarakat mengenai stroke melalui video CHERIE cukup efektif dalam meningkatkan pengetahuan masyarakat.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.005

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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