MétaCan
Menu
Back to cohort
Record W2343111958 · doi:10.5041/rmmj.10238

How Can We Balance Ethics and Law When Treating Smokers?

2016· article· en· W2343111958 on OpenAlexaff
Helen Senderovich

Bibliographic record

VenueRambam Maimonides Medical Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsYork UniversityUniversity of TorontoBaycrest Hospital
Fundersnot available
KeywordsDisadvantageDutyDilemmaBalance (ability)ObligationMedicineEthical dilemmaPopulationMedical ethicsPublic relationsLawPsychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.480
GPT teacher head0.527
Teacher spread0.046 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRambam Maimonides Medical JournalSame topicHealthcare cost, quality, practicesFrench-language works237,207