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Record W7015796234

Training Medical Student Counselors for the Rochester Model, a Hospital Tobacco Treatment Program

2024· article· en· W7015796234 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsPhoneEconomic shortageSmoking cessationQuarter (Canadian coin)University hospitalMedical school
DOInot available

Abstract

fetched live from OpenAlex

John C Grable,1,* Kevin Shan,1,* Matthew Wang,1,* Daniel D Han,1,* Kristen Sportiello,1,* Melissa Chang,1,* Justin R Sysol,1,* Doris Bugbee,2,* Kenneth Peltzer1,* 1Department of Medicine, University of Rochester School of Medicine, Rochester, NY, USA; 2Department of Nursing, University of Rochester School of Medicine, Rochester, NY, USA*These authors contributed equally to this workCorrespondence: John C Grable, University of Rochester School of Medicine, 601 Elmwood Avenue, Rochester, NY, 14642, USA, Tel +1 585-275-7424, Fax +1 585-276-2820, Email john_grable@urmc.rochester.eduPurpose: Providing effective tobacco dependence treatments to hospitalized patients remains a challenge. Prior to 2021, the Rochester Model program used staff nurses for both bedside and post-discharge counseling necessary to maintain abstinence. When nurse shortages and elevated job stress occurred during the COVID Pandemic, we proposed that medical students learn to counsel patients at the bedside and after discharge.Patients and Methods: Due to COVID restrictions, first- and second-year medical students trained using remote Zoom sessions. The total training time was 2.5 hr without role-play or additional evaluations. A survey measured the students’ satisfaction, confidence, and counseling barriers. A smoking patient on a participating hospital unit can enroll in the program. Students delivered bedside counseling, then provided follow-up treatment and outcome calls along with New York State Quitline counselors.Results: The survey demonstrated that 89% of the students were satisfied with the training. The bedside counseling confidence was greater than the phone counseling confidence. All students felt the program experience has value to them as future physicians. 124 smoking patients enrolled, and outcomes followed out to 6 months. The 7-day point prevalence quit rates using the as-treated (patients contacted) analysis were 57% at 4 weeks, 48% at 3 months, and 43% at 6 months. The 7-day point prevalence quit rates using the intent-to-treat (all patients) analysis were 31% at 4 weeks, 16% at 3 months and 14% at 6 months.Conclusion: Medical students given minimal training are effective tobacco cessation counselors at no cost to the hospital system. The Rochester Model program using student counseling benefits patients, the students, and the health-care system.Plain Language Summary: Hospitalization is an opportunity to help smokers quit. Successful programs require both bedside counseling and post-discharge contacts beyond a month. Cost remains the major issue for treating hospitalized smokers. Prior to the COVID Pandemic, the Rochester Model program used hospital nurses as bedside, post-discharge call counselors and champions. However, during the Pandemic, nurse shortages and work stress reduced their participation. Medical students seeking early patient contact trained as counselors, and the program shows promising quit rates at no cost. The Rochester Model supports the real-world application of medical students in hospital tobacco treatment programs.Keywords: tobacco dependence treatment, medical students, nurse counseling, quit-line counseling

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)0.001
metaresearch head score (Gemma)0.002
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 score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.003

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 head0.362
GPT teacher head0.613
Teacher spread0.251 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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