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Enregistrement W4405770208 · doi:10.6000/1929-6029.2024.13.34

Early Detection Model of Drug Abuse Relapse in the City of Padang

2024· article· en· W4405770208 sur OpenAlexvenueno aff
Marryo Borry WD, Rima Semiarty, Hasbullah Tabranny, Effa Yonnedi

Notice bibliographique

RevueInternational Journal of Statistics in Medical Research · 2024
Typearticle
Langueen
DomaineMedicine
ThématiquePublic Health and Nutrition
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRehabilitationAddictionChristian ministryRelapse preventionSubstance abuseMedicinePsychiatryDrugMental healthPhysical therapy

Résumé

récupéré en direct d'OpenAlex

The past year prevalence rate was 1.80% or 180 out of 10,000 Indonesians aged 15-64 years or equivalent to approximately 3.4 million people. The survey also found that drug abuse has penetrated into the countryside with very prominent drug use at a very productive age (25-49 years) and the prevalence rate in the past year of use above 2.5%. The various impacts of drug use can be overcome by conducting a rehabilitation program. The process of drug rehabilitation is a process given to drug addicts so that their mental, physical and social conditions improve, the existence of rehabilitation is expected to be able to reduce the adverse effects on physical and mental conditions and can reduce dependence and relapse due to drug use, so as to reduce the number of drug abusers. In this post-rehabilitation stage, drug abusers are prone to relapse. The case of relapse in drug users is very high, found in more than 50% of addicts in the last decade. Based on research, relapse rates are known to reach approximately 80 percent within the first six months, and occur as much as approximately 50 percent within two years. However, the various definitions of relapse have led to different relapse rates in Indonesia. The Ministry of Health in 2018 claimed that the relapse rate in Indonesia reached 24.3% while the relapse rate according to BNN stated that before the implementation of rehabilitation, Indonesia's relapse rate reached 90%. Indonesia's relapse rate after the implementation of rehabilitation at the Lido Bogor rehabilitation and therapy center is around 7%. Methods: This study uses a qualitative design with a phenomenological approach and aims to determine the determinants of early detection of relapse in drug abusers in Padang city. The informants in this study are drug abuser clients who are undergoing rehabilitation, in the post-rehabilitation program, and who have completed the rehabilitation program at HB Saanin Mental Hospital Padang, West Sumatera BNNP Clinic, and Yayasan Karunia Insani in Padang City, with a total of 6 people. In addition, the respondent sample consisted of 30 drug abusers who were undergoing rehabilitation. The analysis included instrument validity and reliability tests, expert analysis, and diagnostic test analysis. Results: Respondents' ages varied from 18 to 46 years old Factors that encourage relapse are the influence of friends and invitations from friends who use drugs. In addition, the absence of work and family problems also encourage relapse, Family, friends and community support for resilience, Informants revealed that rehabilitation programs can help informants from the risk of relapse, Informants confirmed that relapse can occur in anyone even in people undergoing intensive treatment, comprehensive and sustainable lecture programs can prevent relapse, Stress, depression, and social pressure factors affect the risk of relapse, the first signs of relapse felt by informants are unstable emotions. The developed relapse early detection model has significant predictive ability with an AUC of 78% and can predict the incidence of relapse with an accuracy between 60.2% and 95.8%. The model shows a strong correlation with the SSRS and has a 10,200 times greater chance of detecting relapse cases than the SSRS. Conclusion: Informants define relapse as a situation where someone who has used drugs uses drugs again, Factors that encourage relapse are the influence of friends and invitations from friends who use drugs. In addition, the absence of work and family problems also encourage relapse, Family, friends and community support for resilience, Informants revealed that rehabilitation programs can help informants from the risk of relapse, Informants confirmed that relapse can occur in anyone even in people undergoing intensive treatment, comprehensive and sustainable lecture programs can prevent relapse events, Stress, depression, and social pressure factors affect the risk of relapse, the first signs of relapse felt by informants are unstable emotions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,084
Score d'incertitude au seuil0,168

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,091
Tête enseignante GPT0,479
Écart entre enseignants0,388 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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