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Record W4402136544 · doi:10.3389/fpsyt.2024.1440738

A systematic review of suicide risk management strategies in primary care settings

2024· review· en· W4402136544 on OpenAlexaboutno aff
Monika Sreeja Thangada, Rahul Kasoju

Bibliographic record

VenueFrontiers in Psychiatry · 2024
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOGeneralizability theoryMedicineSystematic reviewSuicide preventionRandomized controlled trialPoison controlMental healthRisk assessmentMEDLINEPsychologyFamily medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Introduction and Objective: Suicide is a major public health concern. Recently, suicide rates have increased among traditionally low-risk groups (e.g., white, middle-aged males). Suicide risk assessments and prevention strategies should be tailored to specific at-risk populations. This systematic review examines suicide risk detection and management in primary care, focusing on treatments to reduce suicide rates and improve prevention efforts. Methodology: A systematic review was conducted following PRISMA guidelines. Literature was collected and analyzed using Boolean operators with relevant keywords in databases (e.g., PubMed, Google Scholar, PsycINFO) to identify randomized and non-randomized studies focusing on suicide risk factors and management strategies in primary care, published in the past 10 years. The risk of bias 2.0 and Newcastle Ottawa scale was used to assess risk of bias, and data from moderate-quality studies were synthesized. Results: Thirteen moderate-quality studies were reviewed. Key findings include the need for assessing modifiable risk factors like substance use and mental health. General practitioner (GP) engagement post-suicide attempt (SA) improves outcomes and reduces repeat SAs. Effective strategies include comprehensive risk assessments, collaborative treatment, and enhanced GP support. Barriers to effective suicide prevention include insufficient information, judgmental communication, lack of positive therapeutic relationships, and inadequate holistic assessments. These findings highlight the need for tailored suicide prevention strategies in primary care. However, the evidence sample size is small with reduced statistical power that limits generalizability. The included studies were also regional examinations, which restrict their broader relevance. Discussion: Significant risk factors, barriers, and effective strategies for suicide prevention were identified. For children aged 12 or younger, preexisting psychiatric, developmental, or behavioral disorders, impulsive behaviors, aggressiveness, and significant stressful life events within the family were critical. For adults, loneliness, gaps in depression treatment, and social factors are significant. Barriers to suicide prevention included insufficient information, judgmental communication, lack of positive therapeutic relationships, inadequate holistic risk assessments, lack of individualized care, insufficient tangible support and resources, inconsistent follow-up procedures, variability in risk assessment, poor communication, stigma, and negative attitudes. Effective methods include the Postvention Assisting Bereaved by Suicide training program, continued education, comprehensive clinical assessments, individualized care, and community-based interventions like the SUPRANET program. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024550904.

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)0.013
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.061
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0130.013
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.325
Teacher spread0.310 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations20
Published2024
Admission routes1
Has abstractyes

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