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Record W2912223297 · doi:10.5539/ass.v15n2p142

Usrah Approach of Drug Addiction Treatment in a Fishermen Community: Psychology of Da’wah Perspective

2019· article· en· W2912223297 on OpenAlexvenueno aff
Wan Hanis Aisyah Wan Rosdi, Zawawi Yusoff, Nasir Mohamad, Shafie Hamzah, S. Masaud

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamAddictionPerspective (graphical)Drug userPsychologyDrug addictSociologyCriminologySocial psychologyDrugPsychiatryPhilosophyTheologyComputer science

Abstract

fetched live from OpenAlex

The Psychology of da’wah (making an invitation) is part of the Islamic psychology approach based on the Al-Quran and Al-Sunnah. Da’wah in terms is invites people to do something benefit and abstain from evil. Psychology of da’wah is a part used in the treatment and rehabilitation of drug addiction to the drug addict. This article discusses the usrah (a meeting involving Islamic religious activities) approach as a means of assisting drug addict in fishermen communities to reduce addiction. The method used in this article is a qualitative study of past documentary literature review analysis and library document analysis. The result of the previous studies show that the psychology of da’wah through the usrah approach is one of the method to implement for rehabilitate the drug addict who ia a muslim. This is because this approach ia a based on Islamic teachings which has been implemented by the Messenger Muhammad Pbuh.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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