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Record W2032812805 · doi:10.1136/ebn.7.2.62

Patients resisted attempts to link smoking to their current medical problems in general practice consultations

2004· letter· en· W2032812805 on OpenAlexaff
Denise S. Tarlier

Bibliographic record

VenueEvidence-Based Nursing · 2004
Typeletter
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineWeb of scienceGeneral practiceGynecologyFamily medicineInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

Pilnick A, Coleman T. “I’ll give up smoking when you get me better”: patients’ resistance to attempts to problematise smoking in general practice (GP) consultations. Soc Sci Med 2003;57:135–45.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In general practice consultations, how do general practitioners (GPs) introduce and advise patients who smoke about quitting? How do patients respond to this advice? Interactional analysis of videotaped patient-GP consultations and semistructured interviews with GPs. General practices in an East Midlands County in the UK. 47 consultations between 47 patients who were self reported regular or occasional smokers {mean age 41 y, 74% women}* and 29 different GPs. 39 GPs were also interviewed. 42 GPs who responded to a postal questionnaire measuring attitudes towards discussing smoking with patients were recruited. Of the 538 video recorded consultations, 47 (between 47 different patients and 29 GPs) that mentioned smoking were chosen for further analysis. In addition, 39 semistructured interviews … [1]: {openurl}?query=rft.jtitle%253DSocial%2Bscience%2B%2526%2Bmedicine%26rft.stitle%253DSoc%2BSci%2BMed%26rft.aulast%253DPilnick%26rft.auinit1%253DA.%26rft.volume%253D57%26rft.issue%253D1%26rft.spage%253D135%26rft.epage%253D145%26rft.atitle%253D%2526quot%253BI%2527ll%2Bgive%2Bup%2Bsmoking%2Bwhen%2Byou%2Bget%2Bme%2Bbetter%2526quot%253B%253A%2Bpatients%2527%2Bresistance%2Bto%2Battempts%2Bto%2Bproblematise%2Bsmoking%2Bin%2Bgeneral%2Bpractice%2B%2528GP%2529%2Bconsultations.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0277-9536%252802%252900336-2%26rft_id%253Dinfo%253Apmid%252F12753822%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/S0277-9536(02)00336-2&link_type=DOI [3]: /lookup/external-ref?access_num=12753822&link_type=MED&atom=%2Febnurs%2F7%2F2%2F62.atom [4]: /lookup/external-ref?access_num=000183191700011&link_type=ISI

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.079
GPT teacher head0.380
Teacher spread0.300 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2004
Admission routes1
Has abstractyes

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