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Record W7071239388

A scoping review of the international evidence-base for developing guidelines for participants and researchers, in conducting research on suicide and self-harm prevention. The evidence for guidelines for suicide research participants and researchers

2022· report· en· W7071239388 on OpenAlexfundno aff

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
FundersCentre for Public Health, Queen's University BelfastQueen's UniversityUlster UniversityQueen's University Belfast
KeywordsCINAHLSuicide preventionInclusion (mineral)Poison controlResearch ethicsHuman factors and ergonomicsPresentation (obstetrics)MEDLINEEmpirical research
DOInot available

Abstract

fetched live from OpenAlex

Objective: To examine the international evidence-base for developing guidelines for participants and researchers, for conducting research on suicide and self-harm prevention. Introduction: The development of guidance materials regarding suicide research can be beneficial for researchers and help both ethics committees and researchers review and conduct suicide-related research. Improved understanding of the ethical considerations, including dialogue between researchers and ethics committees, should sustain and improve the quality of suicide prevention research, a prerequisite to tackle increasing suicide mortality and presentation of self-harm. However, no guidelines specific to suicide and self-harm research exist to inform this process. Inclusion criteria: Empirical studies published in the last ten years and in the English language that assess and report guidance and ethical considerations for the research process in relation to self-harm and suicide, attempted suicide and death by suicide with a specific focus on the welfare and/or support for researchers and/or families participating in this research will be included. Methods: APA PsychArticles, MEDLINE, CINAHL and Web of Science databases will be searched. Following this, all identified citations will be collated and uploaded into Rayyan (Ouzzani et al., 2016) and the full text of selected citations will be assessed in detail against the inclusion criteria. Data will be extracted from papers included in the scoping review by two or more independent reviewers using a data extraction tool developed by the reviewers. Extracted data will be synthesised and presented in diagrammatic or tabular form in a manner that aligns with the objective of the scoping review.

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)0.192
metaresearch head score (Gemma)0.387
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1920.387
Meta-epidemiology (narrow)0.0040.006
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0630.052
Science and technology studies0.0060.006
Scholarly communication0.0190.020
Open science0.0080.011
Research integrity0.0140.009
Insufficient payload (model declined to judge)0.0130.005

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 head0.877
GPT teacher head0.619
Teacher spread0.258 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
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

Citations0
Published2022
Admission routes1
Has abstractyes

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