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Record W3025612835 · doi:10.1017/cem.2020.224

P016: Feasibility of a nurse-led smoking cessation intervention in the emergency department

2020· article· en· W3025612835 on OpenAlexaffabout
A. Tolmie, Rebecca Erker, Andrew Donauer, Emily Sullivan, Thomas R. Graham, Taofiq Oyedokun, James Stempien

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineEmergency departmentLikert scaleSmoking cessationThematic analysisReferralPsychological interventionFamily medicineQuitlineIntervention (counseling)Descriptive statisticsNursingQualitative researchPsychology

Abstract

fetched live from OpenAlex

Introduction: Cigarette smoking is a leading global cause of morbidity and mortality. Multiple studies internationally have established that cigarette smoking prevalence is higher in emergency department (ED) patients than their respective communities. Previously, we demonstrated the smoking prevalence among Saskatoon ED patients (19.6%) is significantly higher than the provincial average (15.1%), and over 50% of smoking patients would be receptive to ED-specific cessation support. The purpose of this project was to identify nurses’ beliefs regarding smoking cessation in the ED, and barriers to implementing it in the department. Methods: A questionnaire was administered to all nurses employed at St. Paul's Hospital ED in Saskatoon assessing attitudes towards ED cessations, as well as the benefit and feasibility of three potential interventions: brief cessation counselling, referral to community support programs, and distributing educational resources. The questionnaire included Likert scale numerical ratings, and written responses for thematic analysis. Thematic analysis was performed by creating definitions of identified themes, followed by independent review of the data by researchers. Results: 83% of eligible nurses completed the survey (n = 63). Based on Likert scores, ED nurses rarely attempt to provide cessation support, and would be minimally comfortable with personally providing this service. Barriers identified through thematic analysis included time constraints (68.3%), lack of patient readiness (19%), and lack of resources/follow-up (15.9%). Referral to community support programs was deemed most feasible and likely to be beneficial, while counselling within the ED was believed to be least feasible and beneficial. Overall, 93.3% of nurses indicated time and workload as barriers to providing ED cessation support during the survey. Conclusion: Although the ED is a critical location for providing cessation support, the proposed interventions were viewed as a low priority task outside the scope of the ED. Previous literature has demonstrated that multifaceted ED interventions using counselling, handouts, and referrals are more efficacious than a singular approach. While introduction of a referral program has some merit, having professionals dedicated to ED cessation support would be most effective. At minimum, staff education regarding importance of providing smoking cessation therapy, and simple ways to incorporate smoking cessation counselling into routine nursing care could be beneficial.

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)0.008
metaresearch head score (Gemma)0.015
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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.199
GPT teacher head0.471
Teacher spread0.272 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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