Bibliographic record
Abstract
Background/Introduction: Smoking is a fundamental risk factor for many chronic, non-communicable diseases and acknowledged as a leading cause of preventable death worldwide. Quitting smoking is the single most effective thing an individual can do to improve their health. The Ottawa Model for Smoking Cessation (OMSC) is a systematic, comprehensive approach to clinical tobacco dependence treatment developed by smoking cessation experts. The mission of OMSC is to assist healthcare organizations and professionals to transform clinical practices appropriate to the treatment of smokers through knowledge translation, implementation support, and quality evaluation. The OMSC aims to assist large numbers of tobacco users by ensuring the provision of effective, evidence-based tobacco-dependence treatment, delivered by knowledgeable healthcare professionals. Purpose: Our primary goal is to support clinicians in identifying and providing evidence-based interventions to a greater number of smokers using a systematic approach, ultimately increasing cessation rates. The OMSC assists providers to identify smoking status of all patients, provide clear, strong, personalized advice to quit, support patients in making a quit attempt, and provide follow-up support. An overview of the OMSC program will be provided to highlight its evidence-based outcomes, review the implementation steps and organizational change elements required to adopt the program, and showcase experiences of implementing the program.
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 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 | 0.026 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".