Sources of Emotional Maltreatment and the Differential Development of Unconditional and Conditional Schemas
Bibliographic record
Abstract
Schema theory posits that experiences of maltreatment result in the early development of maladaptive schemas (EMS; Young, Klosko, & Weishaar, 2003, Schema therapy: A practitioner's guide, The Guilford Press: New York, NY). EMS are organized by conditionality; unconditional schemas are theorized to develop early in childhood predominantly in response to experiences of parenting and conditional schemas are theorized to develop later in life in response to other relationships. Despite this distinction, minimal previous research has investigated their differential development. The current study examined the relative contributions of parental and other (peer and intimate partner) emotional maltreatment (EMT) in the differential development of unconditional and conditional schemas. Ninety-seven undergraduate students retrospectively reported their maltreatment experiences using the Lifetime Experiences Questionnaire and completed the Young Schema Questionnaire to measure EMS. Consistent with hypotheses, parental EMT was the strongest predictor of unconditional schemas. Unexpectedly, parental EMT also emerged as the strongest predictor of conditional schemas. Theoretical and clinical implications of these findings are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".