Training Medical Student Counselors for the Rochester Model, a Hospital Tobacco Treatment Program
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".