L’inégalité de revenus : un « virus » qui affecte la santé mentale et le bonheur
Bibliographic record
Abstract
AIM: The purpose of this paper is to demonstrate the impact of income inequality on various indexes of mental health and on happiness in wealthy nations. Initially, the unequal distribution of income is documented in wealthy nations, especially in the United States of America. After the World War II, income equality was at a level never reached before, but since the eighties, income inequality has raised dramatically in many industrialized countries. The 2008 crisis has worsened the situation in many of them, particularly in the United States. Furthermore, prejudices have increased against women, Blacks, Spanish-speakers and those who receive social welfare. METHOD: A selective review of the literature is made in order to document the impact of income inequality on a few indicators of mental health (from WHO, UN, UNICEF, OCDE and World Bank) and on happiness, defined here as life satisfaction. RESULTS: Income inequality is positively related to the following indexes: Index of Mental Illness from the WHO (0.73), Index of the United Nations' Office on Drug Consumption (0.63) and a composite Index of ten psychosocial problems, constituted by Wilkinson and Pickett, 2013 (0.87). On the other hand, income inequality is negatively associated to the UNICEF Index of Child Well-Being (-0.71). Furthermore, the level of anxiety and of depression is higher in countries where income inequality is greater. The correlation between happiness and income inequality in the 23 wealthy nations is -0.48; this correlation becomes -0.41 after control of the effect of the GNP (Gross National Product). These results support the idea that it is relative income - not absolute income - which matters in the evaluation of our life and of our happiness. In underdeveloped nations, any increase in GNP promotes the well-being of the citizens; whereas in wealthy nations, it is the equality of the distribution that is more important. Many arguments supporting the causal relation from income inequality to psychosocial problems and unhappiness are presented. In reality, this income inequality is like a "virus" which affects the well-being of the entire population. CONCLUSION: Even if the increase of mental problems may be explained by many factors - historical, cultural, ethnic, social, and societal - these factors do not eliminate the effect of income inequality. In order to counter the effects of income inequality and to promote a "flourishing" mental health, the professionals of human sciences are invited to take into account this reality in the implementation of their interventions and to participate to the elaboration of social politics as well as in the education process of the general population.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".