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Record W4306751884 · doi:10.54488/ijcar.2022.297

Research Reactivity and Distress Protocols for Youth Trauma-related Research: A Scoping Review

2022· review· en· W4306751884 on OpenAlexafffundvenue
Angela H. Wei, Marria Khalid, Erik Ge, Jiyeon Kang, Makaśa Looking Horse, Christine Wekerle

Bibliographic record

VenueInternational Journal of Child and Adolescent Resilience · 2022
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of WaterlooMcMaster University
FundersCanadian Institutes of Health Research
KeywordsCINAHLDistressClinical psychologyFeelingMEDLINEMedicinePsychologyPopulationPoison controlPsychiatryPsychological interventionMedical emergencyEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

Objective: To explore literature regarding youth with Adverse childhood experiences (ACEs), their potential reactivity to research, and research trauma mitigation protocols. Methods: A systematic scoping review was conducted in APA PsychInfo, CINAHL, Embase, and OVID Medline. 2 reviewers screened each article for 12 eligible studies. Quantitative and qualitative studies measuring maltreatment and trauma research responses were eligible. Youth were defined as individuals aged 10-19. Results: No study utilized the ACEs questionnaire with research-related stress measures. Among those that included research reactivity measures, various forms of childhood and youth victimization were considered. The majority of participants did not report feeling upset, with many reporting benefits to participation. Information on protocols for managing distress was available for 11 studies, the most common being the provision of a resource helpsheet and/or referral system. Implications: There is no indication of distress following ACEs-related research, with few studies measuring across the research experience. One study measured follow-up for distress and further action. Additional research may be indicated to assess the effectiveness of these protocols in this population with a follow-up assessment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.273
GPT teacher head0.532
Teacher spread0.259 · 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 teacher head, not a consensus.

Study designOther design
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

Citations3
Published2022
Admission routes3
Has abstractyes

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