Universality and Normativity of the Attachment Theory in Non-Western Psychiatric and Non-Psychiatric Samples: Multiple Group Confirmatory Factor Analysis (CFA)
Bibliographic record
Abstract
This study tests for the first time the validity of universality and normativity assumptions related to the attachment theory in a non-Western culture, using a novel design including psychiatric and non-psychiatric samples as part of a comprehensive exploratory and advanced confirmatory framework. Three attachment assessments were distributed to 212 psychiatric outpatients and 300 non-psychiatric samples in Yemen. The results of the fourteen approaches of exploratory factor analysis (EFA) produce a similar result and assertion that the psychiatric outpatients tend to explore attachment outcomes based on multi-methods, while the non-psychiatric samples suggest an attachment orientation based on multi-traits (self-other). The multiple group-confirmatory factor analysis (MG-CFA) demonstrates that the multi-method model fits the psychiatric samples better than the non-psychiatric samples. Equally, the MG-CFA suggests that the multi-traits model also fits the psychiatric samples better than the non-psychiatric samples. Implications of the results 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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".