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Record W7162118588 · doi:10.56326/jpk.v5i2.7100

Stigma Gangguan Mental dengan Sikap Mencari Bantuan Profesional: Studi Korelasional Masyarakat di Kota Makassar

2025· article· W7162118588 on OpenAlexaff
Siti Sri Auliah, Muh. Fitrah Ramadhan Umar, A. Nur Aulia Saudi

Bibliographic record

VenueJurnal Psikologi Karakter · 2025
Typearticle
Language
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsStigma (botany)Social stigmaMental health

Abstract

fetched live from OpenAlex

Tingginya angka permasalahan kesehatan mental memerlukan perilaku yang seimbang, yaitu tingginya tingkat pencarian bantuan tenaga profesional. Namun salah satu hambatan dalam pencarian bantuan profesional adalah stigma gangguan mental yang beredar di masyarakat. Penelitian ini menggunakan metode pendekatan kuantitatif dengan tujuan penelitian melihat adanya hubungan stigma gangguan mental dengan sikap mencari bantuan profesional. Data dikumpulkan dengan skala Stigma Gangguan Mental dan skala Attitudes toward Seeking Professional Psychological Help (ATSPPH-SF) kemudian dianalisis dengan menggunakan uji Normalitas, Linearitas, dan Korelasi Pearson Product Moment. Hasil penelitan ini menunjukkan adanya hubungan negatif yang signifikan antara stigma gangguan mental dengan sikap mencari bantuan profesional pada masyarakat di Kota Makassar. Hasil analisis menunjukkan (r) -0.132 dengan tingkat signifikan sebesar 0.00 (p<0.05). Hal tersebut menunjukkan bahwa H0 ditolak dan Ha diterima, yaitu ada hubungan stigma gangguan mental dengan sikap mencari bantuan profesional pada Masyarakat di Kota Makassar.

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)0.008
metaresearch head score (Gemma)0.020
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 score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
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 head0.026
GPT teacher head0.338
Teacher spread0.312 · 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
Published2025
Admission routes1
Has abstractyes

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