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Record W4221072486 · doi:10.53730/ijhs.v6ns1.5294

Quitting attempts and potential barriers towards tobacco cessation in out patients: A retrospective analysis

2022· article· en· W4221072486 on OpenAlexaboutno aff
Farhat Yaasmeen Sadique Basha, Sri Sakthi, S Sudharrshiny

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersSaveetha Dental College
KeywordsMedicineAbstinenceSmoking cessationTobacco useQuit smokingTest (biology)Statistical analysisChi-square testStatistical softwareFamily medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Background and Aim: Tobacco use is one of the leading global causes of avoidable death worldwide and a major risk factor for the development of cardiovascular diseases, chronic obstructive pulmonary diseases and cancer. A study in Canada reported that 64.4% of the people that used tobacco wanted to quit. Unfortunately only less than 5% of such attempts actually lead to long-term abstinence. This study aims to evaluate the quitting attempts and various barriers faced by patients when attempting tobacco cessation. Methodology: In this study we evaluated the records of 354 different patients that underwent Anti-tobacco counselling in Saveetha dental college and hospitals to determine the number of quitting attempts made and some common barriers faced by them in the process. Statistical analysis was done using SPSS software. The statistical analysis for correlation was done using Perason’s Chi-Square test and Cross tabulation was done. Age and Gender were considered as independent variables. Results: The results showed that 28.81% of the patients that reported to the clinic were from the age group 31-40 years. Only 5.93% of the patients had made more than two attempts to quit tobacco. 44.07% of the patients showed a cessation period of less than 6 months.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.364
Teacher spread0.338 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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