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Upaya Peningkatan Kapasitas Guru SMP Pius Cilacap dalam Mendampingi Remaja Sebagai Generasi Digital Native untuk Memelihara Kesehatan Reproduksi

2023· article· id· W4406428291 on OpenAlexaff
Catharina Widiartini, Fajar Wahyu Pribadii, Agus Budi Setiawan, Rizak Tiara Yusan, Thomas Sutasman

Bibliographic record

VenueLinggamas Jurnal Pengabdian Masyarakat · 2023
Typearticle
Languageid
FieldHealth Professions
TopicAdolescent Health and Behaviors
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Generasi digital native berpotensi menyerap informasi salah dari internet mengenai kesehatan reproduksi. Guru perlu mengoptimalkan kelebihan mereka sebagai tenaga professional yang dekat dengan remaja, yakni kemampuan menyampaikan informasi ilmiah kepada siswa dengan memperhatikan aspek perkembangan psikologis usia remaja awal. Kegiatan pengabdian masyarakat di SMP Pius Cilacap bertujuan untuk menjawab kebutuhan peningkatan kapasitas guru dalam menjalankan peran sebagai sumber informasi dan fasilitator diskusi dengan siswa dan orangtua siswa terkait pemeliharaan kesehatan reproduksi remaja. Workshop dilaksanakan melalui penyampaian materi aspek medis dan psikologis kesehatan reproduksi remaja serta sesi bermain peran, dengan fokus pada penerapan teknik I message. Terdapat peningkatan pengetahuan sebesar 19,42 poin atau sebesar 24,72% pada post-test dibandingkan pre-test. Pihak mitra menyatakan kepuasan mereka atas penyelenggaraan kegiatan tersebut dan mengharapkan agar kegiatan serupa dapat dilaksanakan secara serial. Fokus topik kesehatan reproduksi remaja yang dapat diprioritaskan adalah terkait topik hubungan seksual dan kehamilan serta topik pubertas pada perempuan dan menstruasi.

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)0.006
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.004
Science and technology studies0.0060.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0030.014

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 head0.072
GPT teacher head0.388
Teacher spread0.316 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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