Implementation of an electronic referral smoking cessation tool into clinical practice across cancer clinics of different disease sites and institutions.
Bibliographic record
Abstract
112 Background: Cancer Care Ontario, the government's cancer management agency, mandated routine collection of smoking data province wide (catchment of > 13 million individuals), but did not mandate specifics of a smoking cessation program for cancer patients, despite known benefits. Referral rates to smoking cessation clinics for cancer patients are low. In 2014, Princess Margaret Cancer Centre developed an electronic, patient-driven e-referral tool shown to improve referral rates, known as the smoking Cessation E-referrAl SystEm (CEASE). The objective of this study is to assess the feasibility for expanding this program through adoption and implementation of the CEASE tool to 5 additional clinics across 3 different Cancer Centres. Methods: The Canadian Institute of Health Research’s Knowledge-to- Action (CHIR KTA) framework guided the assessment. Feasibility was addressed through situational assessments, pilot implementation, and clinic/patient feedback in at least two cycles, with procedural changes made at the conclusion of each cycle. Clinics were selected to cover a wide range of hospital, clinic styles, and disease sites. Results: Pilot implementation occurred over 21 clinic days (Jun 2017- Aug 2017); 123 patients were enrolled in the pilot studies in breast, gastrointestinal, head and neck, and thoracic sites. Feasibility assessment in each clinic identified similarities and differences in resource availability, access to authority figures, and patient flow patterns. After the feasibility assessment, a pilot implementation identified English as a second language and the lack of patient's tablet/technological experience as the main barriers for implementation in 4 of 5 clinics. The use of volunteers assisting patients with the survey helped address these barriers. Strong internet connection, short survey length and staff engagement were seen as universal common facilitators to implementation in all 5 clinics. Conclusions: Despite different outpatient environments, common barriers and potential solutions were identified, which will help with scalable future widespread implementation of CEASE across the province of Ontario.
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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 | 0.059 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".