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Record W2998283135 · doi:10.5539/gjhs.v12n1p139

Effect of Smoking on Appetite, Concentration and Stress Level

2019· article· en· W2998283135 on OpenAlexaffvenue
Ahmed T. Awad, Atef Obayan, Suzana Salhab, Rabih Roufayel, Seifedine Kadry

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAppetiteMoodMedicineCigarette smokingFeelingStress (linguistics)Poor AppetitePsychologyClinical psychologyInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Objective: Smokers often report that cigarette relieve feeling of stress, improve mood and concentration and can decrease their appetite level. To identify weather a cigarette is a mood altering and appetite suppressant we study the effect of smoking on concentration, stress and appetite level among smokers. Design: We examined if there is a relation between smoking and other variables (age, gender and working hours per week). Several data collected in the form of surveys from smokers and non-smokers and then analyzed using a software program SPSS. Main outcome: Results according to smoker's majority shows that cigarette decrease their stress level and it has been shown that it’s the most affected parameters compared to concentration and appetite level that are affected also by smoking. Results: The results of this study show that smoking is related to age and it is affected by the number of working hours. Participants aged between 14 and 35 years, that include students, unemployed and hard-workers smoke the most and have the highest number of cigarettes per week. Conclusion: Based on our study, smoking has an effect on appetite, concentration and stress that is correlated with working hours.

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)0.006
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 score0.024
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.453
Teacher spread0.404 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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