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30 años de investigación en maltrato infantil: un análisis bibliométrico (1991-2020)

2023· article· es· W4387761133 on OpenAlexaboutno aff
Maribel Vega-Arce, Gastón Núñez-Ulloa, Francisca Kanelos Torres, Gonzalo Salas, Miguel Barboza-Palomino, Wilson López‐López, Yuh‐Shan Ho

Bibliographic record

VenueSuma Psicológica · 2023
Typearticle
Languagees
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePsychologySexual abusePoison controlMedicinePhilosophyHuman factors and ergonomics

Abstract

fetched live from OpenAlex

Introduction: Child abuse is a global problem that affects children’s development and can have consequences throughout their lives. Despite the need to research to eradicate this phenomenon, there has not been a bibliometric approach to its recent advances. Method: This study examined 16,708 articles on child abuse indexed in the Web of Science between 1991 and 2020 to establish research trends. Results: Child maltreatment is an active field, mainly influenced by the USA, Canada and the UK, and journals in the family studies category, especially Child Abuse & Neglect. The most relevant articles address the topic as part of Adverse Childhood Experiences or focus on its assessment. Considering the most relevant articles, the most studied topics (subjects, research, maltreatment and types of maltreatment, family and parenting, and disorders) and the main foci (maltreatment, research, sex, reporting, and sexual abuse), it is possible that the area is being restructured under the ecobiodevelopmental perspective, with emphasis on treatment and prevention. Its theoretical and practical implications are discussed. Conclusions: This study updates research trends in the field of child maltreatment, providing a comprehensive overview that suggests an evolution toward the integration of multiple disciplines and approaches. The results highlight the importance of further research on this global problem, as well as the need to evaluate existing interventions to reduce its impact on children’s development.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.017
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.033

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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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