Examining the Role of the Health Care Professional in Controlling the Tobacco Epidemic: Individual, Organizational and Institutional Responsibilities
Bibliographic record
Abstract
Despite a historical inclination to view tobacco use as a defect in character or reason, advances in the neurobiological understanding of human behavior and the associated disturbances in behavior patterns produced by exposure to nicotine lead to a conceptualization of tobacco dependence as a chronic disease of the brain. Far from being ineffective, the health care practitioner is in a supremely enabled position to effect change, given the enormous access health care has to dependent patients and the established models of longitudinal care of chronic illness. Our understanding of the biology of addiction, as well as the availability of effective methods of treatment, create a fiduciary responsibility to patients suffering from the addiction - a responsibility which is difficult to ignore given the magnitude of the problem. Until recently, the focus of change within health care has been on promoting new individual and organizational roles for the health care professional caring for tobacco-dependent patients. This chapter explores those roles more fully and suggests new ways of imagining these responsibilities. In addition, this chapter will explore the nature of the institutional role of health care in establishing cultural norms and expectations that are most likely to influence the future trajectory of the epidemic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".