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Record W4388422174 · doi:10.5152/addicta.2023.22098

Evaluation of the Behaviors of Physicians Working in Primary Healthcare Institutions About Secondhand Smoke

2023· article· en· W4388422174 on OpenAlexaff
Fatma Nur Karaçorlu, Edibe Pi̇ri̇nçci̇

Bibliographic record

VenueAddicta The Turkish Journal on Addictions · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsSecondhand smokeHealth carePrimary carePrimary health careEnvironmental healthSmokeBusinessMedicineFamily medicineEconomic growthWaste managementEngineeringEconomics

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the behaviors of physicians working in primary healthcare institutions in Elazığ province about secondhand smoke. This cross-sectional study was conducted on 250 physicians working in primary healthcare institutions in Elazığ. For data collection, a questionnaire was used. Obtained data were evaluated with frequency, percentage, mean ± standard deviation, chi-square, Mann– Whitney U-test, and binary logistic regression analysis. The mean age of the physicians was 40.86 ± 10.58 years and 68.0% of them were male. Of the physicians, 12% received training on secondhand smoke, 13.2% asked patients about secondhand smoke exposure, and 34.8% advised that patients be protected from secondhand smoke exposure. Male physicians (odds ratio: 3.00, 95% Confidence Interval: 1.10–8.18) and physicians trained in secondhand smoke (odds ratio: 3.55, 95% Confidence Interval: 1.44–8.78) stated that they asked patients more frequently about their exposure to secondhand smoke. As a result, very few of the primary care physicians ask about the exposure of their patients to secondhand smoke and very few of them have received training on secondhand smoke. The number of trained physicians should be increased in order for physicians to ask patients about secondhand smoke exposure and to provide counseling on this issue.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.105
GPT teacher head0.364
Teacher spread0.259 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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