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Record W3139163335 · doi:10.1177/0044118x211002857

The Relationship Between Self-Compassion, Childhood Maltreatment and Attachment Orientation In High-Risk Adolescents

2021· article· en· W3139163335 on OpenAlexaffabout
Heather Quinlan, Kellie L. Hadden, David P. Storey

Bibliographic record

VenueYouth & Society · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMemorial University of NewfoundlandSt. John’s Health Sciences Centre
Fundersnot available
KeywordsSelf-compassionPsychologyDistressAnxietyClinical psychologySuicide preventionPoison controlDevelopmental psychologyPsychiatryMedicineMindfulnessMedical emergency

Abstract

fetched live from OpenAlex

The purpose of the current study was to explore whether selfcompassion predicted psychological distress over and above childhood maltreatment and attachment orientation in high-risk youths. Fifty-one youths (31 males, 20 females) aged 17 to 24, recruited from a community non-profit organization in St. John’s, Newfoundland and Labrador, Canada, were administered validated measures of childhood maltreatment, attachment orientation, self-compassion, and psychological distress. Results indicated that self-compassion was inversely associated with childhood maltreatment, attachment anxiety, attachment avoidance, and psychological distress. However, results did not support the hypothesis that self-compassion was a significant predictor of psychological distress over and above attachment anxiety and childhood maltreatment in high-risk youths. Our results indicated that self-compassion is not well developed in street-involved youths and may be a vital intervention target to heal negative internalized views of the self, while maintaining vigilance to threats inherent in the street environment.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.316
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2021
Admission routes2
Has abstractyes

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