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Promotive and preventive programs about basic life support in Medan Barat district

2021· article· en· W3137776615 on OpenAlexaboutno aff
Bastian Lubis, Putri Amelia, Ali Nafiah Nasution, Melati Silvanni Nasution, Silvanni Nasution

Bibliographic record

VenueABDIMAS TALENTA Jurnal Pengabdian Kepada Masyarakat · 2021
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsBasic life supportLife savingBasic educationFirst aidData collectionLife supportMedicinePsychologyNursingFamily medicineGerontologyMedical emergencySociologyPedagogyEmergency medicineSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Cardiac arrest remains a prominent public health problem and cause of death globally. Despite there is no national data of Indonesia available currently, around 350,000 people in the United States and Canada experienced an arrest every year, and half of them were dead. The community knowledge and awareness about basic life support, particularly among mothers, is still low. Therefore, they cannot contribute effectively in providing first-aid to reduce the mortality. To improve this situation, we need an education and training program about basic life support for the PKK mothers and Posyandu cadres in West Medan to reduce the mortality rate of cardiac arrest in Indonesia. The activity was conducted in several stages, from basic data collection, lectures, basic life support practical training, evaluation and guidebooks handover. It was attended by 26 people, 15 women (57.7%) and 11 men (42.3%), with a mean age of 39 years old. No significant change was found in the level of knowledge and behavior before and after the activity (Z-score: 0,001; p= 1,000).

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)0.000
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.284
Teacher spread0.264 · 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
Published2021
Admission routes1
Has abstractyes

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