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Record W1975279680 · doi:10.2105/ajph.2007.121954

ANOTHER EXAMPLE OF AN ILLICIT CIGARETTE MARKET: A STUDY OF PSYCHIATRIC PATIENTS IN TORONTO, ONTARIO

2007· letter· en· W1975279680 on OpenAlexaffabout
Russell C. Callaghan, Joey Tavares, Lawren Taylor

Bibliographic record

VenueAmerican Journal of Public Health · 2007
Typeletter
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychiatryMedicineEnvironmental health

Abstract

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Tobacco taxation is a key mechanism for reducing smoking consumption and prevalence in the general population. Few studies, however, have acknowledged the disproportionately heavy tobacco tax burden placed upon some groups—usually poor, marginalized populations—in the drive for population-based public health goals.1 Shelley et al.2 recently examined the relations between a large tax increase in New York State and the development of a pervasive, illicit cigarette market in a low-income minority community and described the financial burden of smoking among the poor who had not quit. Our letter extends these qualitative findings in 2 ways: by examining similar issues in a different marginalized, low-income population—psychiatric patients in one of Canada’s largest psychiatric hospitals—and by quantifying the relative magnitude of illicit cigarette consumption in this population. Approximately 60% to 80% of people with schizophrenia and other severe mental illnesses smoke cigarettes3,4—a rate of roughly 4 times higher than that of the general population in Canada.5 Smoking plays an important role in the significantly higher rates of coronary heart disease morbidity and mortality found among people with severe mental illnesses, and coronary heart disease screening and smoking-cessation programs are much needed for this population.6 Our study involved the collection of cigarette butts from 3 sites in Toronto, Ontario: a 436-bed inpatient psychiatric hospital (where 70–75% of the patients have a primary diagnosis of schizophrenia), an addiction and mental health research and outpatient facility, and a large general hospital. In addition, a garbage audit was performed at the inpatient psychiatric facility to extract cigarette packages from 1 week’s worth of garbage. The collected cigarette butts were then sorted according to their filter-tip logos. In Ontario, an unbranded cigarette filter almost always indicates an illicit brand. The inpatient psychiatric hospital had a dramatically higher rate of “unbranded” cigarette butts: 54% versus 16% at the research facility and 6% at the general hospital site (Figure 1 ▶). The garbage audit resulted in the extraction of 320 cigarette packages. Approximately 80% of the packages were from illicit tobacco brands—a rate dramatically higher than that found in a recent, city-wide garbage audit in Toronto.7 FIGURE 1— Patterns of branding on cigarette butts collected at an inpatient psychiatric hospital (n = 1288 butts), an addiction and mental health research and outpatient treatment facility (n = 1271 butts), and a general hospital (n = 1784 butts). Similar to the findings of Shelley et al., our study demonstrated that cigarette taxation policies appear to place a disproportionate burden on some marginalized groups. As a result, it is important to assess and ensure principles of taxation equity for such populations,8 especially given that higher cigarette prices may paradoxically increase the availability of cheap (but illicit) cigarettes and undermine smoking-related interventions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.005
Science and technology studies0.0090.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.335
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2007
Admission routes2
Has abstractyes

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