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Record W7056238375

Evaluating Outcome Factors of Childhood Emotional Neglect: An Exploratory Factor Analysis

2022· article· en· W7056238375 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons at National Lewis University (National Lewis University) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaFeelingAnxietyNeglectExploratory factor analysisExploratory researchIntrospectionAttributionPoison controlQualitative analysis
DOInot available

Abstract

fetched live from OpenAlex

Experiencing childhood maltreatment has been shown to have major implications on adult outcomes. Less is known about the outcomes of childhood emotional neglect (CEN). The purpose of this study was to identify factors related to psychological outcomes of CEN within the domains of depression, anxiety, and alexithymia as a precursor to the development of an inventory. One hundred and fifty participant responses on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Toronto Alexithymia Scale-20 (TAS-20) were collected. Exploratory factor analysis was conducted where nine factors yielded significant results and were titled, “Depressive Symptoms,” “Difficulty Identifying Feelings,” “Usefulness of Feelings,” “Difficulty Describing Feelings,” “Psychosomatic Symptoms,” “Reduced Physical Activation,” “Avoidance of Symbolism in Entertainment Preferences,” “Externality,” and “Anxiety Symptoms,” respectively. This study augments prior literature regarding CEN to demonstrate constructs such as the belief in the usefulness of feelings in problem solving (Factor 3), avoiding entertainment that may have deeper meanings (Factor 7), and an avoidance of engaging in introspection (Factor 8). These results demonstrate that outcomes of CEN may be more complex than previously understood.

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)0.000
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0440.000

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 head0.051
GPT teacher head0.280
Teacher spread0.229 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

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