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Hospital doctors' attitudes toward giving their patients smoking cessation help

2007· article· en· W2167533853 on OpenAlexaboutno aff
Thy Thy, Tordis Böker, Frode Gallefoss, Per Bakke

Bibliographic record

VenueThe Clinical Respiratory Journal · 2007
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNorwegianSmoking cessationFamily medicineQuit smokingQuarter (Canadian coin)Health professionalsHealth care

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: While guidelines recommend all doctors to ask their patients about their smoking habits and, in case they smoke, offer cessation advice, limited data are available about hospital doctors' attitudes toward these recommendations. We aimed to examine hospital doctors' attitudes toward asking their patients about their smoking habits, informing about the health benefits of stopping, and offering help to quit smoking. MATERIALS AND METHODS: A random sample (n = 1025) of Norwegian hospital doctors was mailed a questionnaire on this topic. After two reminders, 76% responded. RESULTS: Among the respondents 23% of the doctors found it too time consuming to ask if the patient smoked, and approximately 35% found it too time consuming to inform or offer help on smoking cessation. About 25% of the doctors felt that they did not possess enough knowledge to help the patient to stop smoking, and 65% of the doctors preferred to refer to a specialist for this. Twenty-eight per cent of the doctors did not see it as their task to help the patient to stop smoking, while 32% did not think it is worth the effort to offer the patient help to stop smoking. Twice as many non-internists as internists regarded it as not their task to ask about smoking and advised on smoking cessation. CONCLUSION: In conclusion, about one-quarter to one-third of Norwegian hospital doctors seem to disagree with current guidelines that all doctors should address their patients' smoking habits.

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)0.002
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 score0.077
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.104
GPT teacher head0.403
Teacher spread0.299 · 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

Citations23
Published2007
Admission routes1
Has abstractyes

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