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Record W2094935982 · doi:10.1207/s15327655jchn1802_05

A Smoking Reduction and Cessation Program With Registered Nurses: Findings and Implications for Community Health Nursing

2001· article· en· W2094935982 on OpenAlexaffabout
Karen Chalmers, Ina J. Bramadat, Brenda Cantin, Donna Murnaghan, Elaine Shuttleworth, Shannon D. Scott, Douglas J. Tataryn

Bibliographic record

VenueJournal of Community Health Nursing · 2001
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAttritionMedicineSmoking cessationPsychological interventionNursingCommunity healthFamily medicinePublic health

Abstract

fetched live from OpenAlex

A smoking reduction and cessation program was implemented with registered nurses in 3 Canadian provinces. Nurses (n = 117) participated in either an 8-week group or self-directed program using a resource specifically designed for nurses. Questionnaires were administered prior to and at the end of the 8-week interventions and at 6 and 12 months postintervention. Statistically significant changes at 8 weeks in nurses' smoking practices were found on the number of nurses continuing to smoke, mean number of cigarettes smoked, and movement in the stage of behavioral change. Attrition and variation in patterns of quitting over the 12-month study period made assessing participants' longer term outcomes difficult. This study highlights the complexity of assisting nurses to quit smoking and of implementing and evaluating a program based on accepted community health models of practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.764
Threshold uncertainty score0.476

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.195
GPT teacher head0.532
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 source (direct Gemma or distilled Codex), 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

Citations28
Published2001
Admission routes2
Has abstractyes

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