Bibliographic record
Abstract
A physician is a valued member of society on whom many individuals rely for both professional advice and support during times when they may feel to be at a disadvantage, whether it be physically or mentally. An issue on the rise today concerns the population of smokers in our society. Many are coming to share the opinion that physicians should not provide treatments for smokers. Some of the opinions are based on the claim that smokers are morally responsible for their medical conditions. But, providing care in a fair manner includes not treating differently those who suffer from addiction. Moreover, it is important to recognize that allocating medical resources based on moral responsibility will undermine the physician-patient relationship which is necessary for the practice of medicine. Many countries have codes and policies that physicians must legally follow in terms of providing treatments. With acceptance of the fact that the patient may be unable to execute the decisions made by the physician, it is the legal duty of the physician to provide care and not abandon the patient. An analysis of the many policies around the world brings forward certain changes that must be made in order to make sure that physicians fulfil their legal duty, which is to provide care. As such, this article looks into the existing ethical dilemma in treating smokers around the world, with a review of some policies that will guide our approach in this matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.034 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".