Cross-Cultural Insights from Two Global Mental Health Studies: Self-Enhancement and Ingroup Biases
Bibliographic record
Abstract
Abstract This commentary highlights two cross-cultural issues identified from our global mental health (GMH) research, RECOLLECT (Recovery Colleges Characterisation and Testing) 2: self-enhancement and ingroup biases. Self-enhancement is a tendency to maintain and express unrealistically positive self-views. Ingroup biases are differences in one’s evaluation of others belonging to the same social group. These biases are discussed in the context of GMH research using self-report measures across cultures. GMH, a field evolving since its Lancet series introduction in 2007, aims to advance mental health equity and human rights. Despite a 16.5-fold increase in annual GMH studies from 2007 to 2016, cross-cultural understanding remains underdeveloped. We discuss the impact of individualism versus collectivism on self-enhancement and ingroup biases. GMH research using concepts, outcomes, and methods aligned with individualism may give advantages to people and services oriented to individualism. GMH research needs to address these biases arising from cross-cultural differences to achieve its aim.
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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 | 0.062 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".