Understanding and navigating the repercussions of the politically polarized climate in mental health
Bibliographic record
Abstract
The world is experiencing a moment of political polarization between liberal and conservative ideas, which has aggravated since the arrival of the Covid-19. Many countries (Brazil included) have been experiencing the generalized occurrence of people fighting over politics, in contexts including family, workplace, friendships, and romantic relationships. Over the past 2 years, it has been possible to observe an unexpected and overwhelming effect of the political climate on psychotherapy patients, some of whom have started to actively look for therapists who share their convictions. Brazil is experiencing a moment of severe sanitary, economic, social, and political crisis, which is directly affecting our patients. Nevertheless, the impact of the political climate on our population has not been systematically investigated. However, as the political environment is an inherent part of the social component of the psychosocial model, it is important that mental health professionals be prepared to have this conversation with their patients. This highlights the need to address these difficulties in supervision, rounds, and clinical discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".