The Trans Pacific Partnership Agreement: Exacerbation of inequality for patients with serious mental illness
Bibliographic record
Abstract
Negotiations for a treaty that is set to become one of the world’s biggest trade agreements, the Trans Pacific Partnership Agreement (TPPA), have sparked considerable concern and debate about the possible impacts on health. The TPPA negotiations involve a diverse set of 12 countries from around the Pacific Rim. These include developed countries such as Australia, New Zealand, the United States and Canada, along with much lower-income countries such as Vietnam and Peru.While few details about the nego-tiations are publicly available, the TPPA is said to comprise approxi-mately 29 chapters, which include legal rules covering issues such as investor protections, intellectual property rules and regulatory coher-ence along with more traditional trade issues such as the removal of tariffs. A number of recent reviews based on leaked negotiating docu-ments conclude that there are legiti-mate concerns about the potential impact of the TPPA in relation to ensuring equitable access to medi-cines and public health regulation, including tobacco, food and alcohol regulation (see, for example, Hirono et al., 2014; Wyber and Perry, 2013).While many of the health-related impacts of the TPPA can be expected to be population-wide, many of the impacts will be differentially distrib-uted. People in low-income countries and disadvantaged groups within par-ticipant countries, including those of low socioeconomic status, Indigenous people and those with chronic ill-nesses and disabilities, are likely to be disproportionately affected (Gleeson et al., 2013; Hirono et al., 2014).The purpose of this article is to consider the likely implications of the TPPA on access to health care and public health initiatives (proposed and actual) to improve the health and lifes-pan of patients suffering from serious mental illness (SMI). SMI includes schizophrenia and related disorders, bipolar disorder, depressive disorder, neurotic disorder and substance use disorder. One of the most consist-ently replicated findings in the social sciences has been the negative rela-tionship of socioeconomic status and SMI, indicating that people with SMI face higher levels of disadvantage com-pared to most other groups in the community (Muntaner et al., 2004).
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 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".