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Record W4407035836 · doi:10.61838/kman.psynexus.1.1.15

Psychological Interventions for Early Life Trauma in the Digital Age Childhood

2023· article· en· W4407035836 on OpenAlex
Mehdi Rostami, Amna Arif

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKMAN Counseling and Psychology Nexus · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPsychological traumaPsychologyDevelopmental psychologyClinical psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

The article explores the implications of early life trauma on children's development and the potential of digital technologies to support therapeutic interventions. It begins by outlining the significance of addressing early life trauma, noting the psychological, cognitive, and social challenges that affected children face. The review then delves into current digital interventions, highlighting the benefits and limitations of internet-delivered therapies, mobile apps, and virtual reality in treating and supporting young trauma survivors. It emphasizes the importance of evidence-based, accessible, and engaging digital solutions tailored to the specific needs of children. The discussion extends to ethical considerations, data privacy, and the necessity for professional training in digital tools. Recommendations include developing inclusive digital interventions, fostering interdisciplinary collaboration, and ensuring long-term support for children. The article concludes with a call for continued research, policy development, and the integration of digital innovations into trauma care, advocating for a holistic approach that encompasses the physical, emotional, and social well-being of children in the digital age.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.120
GPT teacher head0.445
Teacher spread0.325 · 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