Evaluating the efficacy of a community participatory intervention to prevent suicide in Thailand: a randomised controlled trial protocol
Bibliographic record
Abstract
INTRODUCTION: The age-standardised suicide mortality rate in Thailand has been stable at a high level in recent years, highlighting the need for suicide prevention interventions. In Thailand, community involvement plays a key role in health promotion. The aim of this ongoing trial is to evaluate the efficacy of a community participatory intervention in two subdistricts in Thailand for reducing suicidality symptoms among individuals considered at high risk for suicide and compare the outcomes to two control subdistricts. METHODS AND ANALYSIS: In this cluster (subdistrict) randomised controlled trial, we randomised two districts to either the community participatory intervention arm or the control arm. From each district, we selected one large and one small subdistricts. We estimated that we need 235 participants per study arm, who were recruited from subdistrict health centres. The primary outcome is suicidality symptoms. Secondary outcomes are depression symptoms, quality of life, stress level and health and community service accessibility. ETHICS AND DISSEMINATION: This trial has been approved by the Research Ethics Committee, Faculty of Nursing, Chiangmai University (number 050/2022). All participants were required to provide informed consent. The findings of the study will be disseminated in peer-reviewed journals and via conferences. TRIAL REGISTRATION NUMBER: TCTR20220620003; the Thai Clinical Trials Registry.
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 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 | 0.043 | 0.031 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".