Pain relief as a way to legitimate human rights
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND OBJECTIVES: Chronic pain is a complex and multidimension problem with consequences for its appreciation in different social segments. It is necessary to understand how human rights may provide basis for political health actions on the subject. This study aimed at evaluating how the legitimation of human rights to the access to chronic pain management is being dealt with. CONTENTS: Research tools were literature review and documental analysis. Due to the qualitative model used, we decided to follow recommendations of the Standards for Reporting Qualitative Research, available in http://www.ncbi.nlm.nih.gov/pubmed/24979285. Data were collected via Internet and statements, legislations and conferences related to human rights and health in chronic pain were included. Data were compared to national and international health policies, involving data from the Department of Health database. After analysis of human rights health promotion documents, we have identified that the Declaration of Montreal of 2010, developed by the International Association for the Study of Pain, has the foundations for political actions to manage and control chronic pain. Notwithstanding the participation of Brazil in support to this Declaration, there are still few concrete actions to implement strategies proposed by the model. We have also identified the major socio-economic impact of chronic pain on Brazilian contemporary society. CONCLUSION: Chronic pain should be studied and managed as a public health problem and health policies need to support this human right with further efficiency.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".