MODELS OF PUBLIC MENTAL HEALTH IN PRACTICE: A SCOPING REVIEW
Bibliographic record
Abstract
Background: Public mental health models are critical for mitigating diverse mental healthcare needs. However, there is limited understanding of effective models in practice. Purpose: This scoping review aims to analyse various existing models of public mental health implementation globally and in India. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed and Google Scholar, using the keywords: ("public mental health" OR "community mental health") AND ("models" OR “frameworks”). The literature search identified 60 potential manuscripts. After screening and full-text assessment, 31 manuscripts were selected for analysis, providing insights into public mental health models. The selection process involved excluding manuscripts not relevant to the research question (n=2), older studies (n=11) and those with inaccessible full texts (n=16). Results: The scoping review identified several effective models: community-based (initiatives and awareness campaigns), settings-based (hospital, school, and workplace programs), crisis intervention (disaster support and suicide prevention), digital (telehealth and mobile applications), and peer support models. Conclusions: There is an urgent need to integrate diverse models of public mental health models into healthcare services. It is crucial in order to effectively address mental health needs and improve outcomes. Abstrak Latar Belakang: Model kesehatan mental publik sangat penting untuk mengurangi beragam kebutuhan perawatan kesehatan mental. Namun, pemahaman tentang model yang efektif dalam praktiknya masih terbatas. Tujuan: Tinjauan cakupan ini bertujuan untuk menganalisis berbagai model implementasi kesehatan mental publik yang ada secara global dan di India. Metode: Pencarian literatur yang komprehensif dilakukan di berbagai basis data, termasuk PubMed dan Google Scholar, menggunakan kata kunci: ("kesehatan mental publik" ATAU "kesehatan mental komunitas") DAN ("model" ATAU "kerangka kerja"). Pencarian literatur mengidentifikasi 60 manuskrip potensial. Setelah penyaringan dan penilaian teks lengkap, 31 manuskrip dipilih untuk dianalisis, yang memberikan wawasan tentang model kesehatan mental publik. Proses seleksi melibatkan pengecualian manuskrip yang tidak relevan dengan pertanyaan penelitian (n=2), penelitian lama (n=11) dan penelitian dengan teks lengkap yang tidak dapat diakses (n=16). Hasil: Tinjauan cakupan mengidentifikasi beberapa model yang efektif: berbasis komunitas (inisiatif dan kampanye kesadaran), berbasis lingkungan (program rumah sakit, sekolah, dan tempat kerja), intervensi krisis (dukungan bencana dan pencegahan bunuh diri), digital (telehealth dan aplikasi seluler), dan model dukungan sebaya. Kesimpulan: Ada kebutuhan mendesak untuk mengintegrasikan berbagai model kesehatan mental publik ke dalam layanan kesehatan. Hal ini penting untuk mengatasi kebutuhan kesehatan mental dan meningkatkan hasil secara efektif.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".