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Record W3215321868 · doi:10.29303/rengganis.v1i2.87

A Pelatihan Aplikasi Perkantoran untuk Meningkatkan Keterampilan Kader Posyandu Anyelir 8 Perumnas Antang Kota Makassar

2021· article· en· W3215321868 on OpenAlexaff
Nuraida Latif, Muhajirin Muhajirin, Mashud Mashud, P Ramlah, Markani Markani, Neneng Awaliah, Butsiarah Butsiarah

Bibliographic record

VenueRengganis Jurnal Pengabdian Masyarakat · 2021
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCommunity serviceMicrosoft OfficeService (business)Microsoft excelComputer scienceEngineeringWorld Wide WebBusinessOperating systemPolitical scienceMarketingPublic relations

Abstract

fetched live from OpenAlex

Posyandu is the center of community activities in the effort to provide health services and family planning. These trained Posyandu cadres are not only seen from the way they handle maternal and child health but also have to be supported by their ability to use computers in administrative management and data processing. The method used in this community service activity is a combination of tutorials, practice, and discussion or question and answer, as well as evaluation to determine the level of absorption of the training material. The office application materials provided are Microsoft word, Microsoft excel, and Microsoft powerpoint. Office application program training activities for cadres of Posyandu Anyelir 8 Block 8 Perumnas Antang were carried out well and improved the skills of Posyandu Anyelir 8 Block 8 Perumnas cadres in the use of information technology.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0560.009

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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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