It’s Not That Simple: Tobacco Use Identification and Documentation in Acute Care
Bibliographic record
Abstract
This environmental telephone interview scan was designed to identify: (1) how hospitals in one Canadian province incorporated tobacco use identification/documentation systems into practice; and, (2) challenges/issues with tobacco identification/documentation. Participants included 36/139 hospitals previously identified to offer cessation services. Results showed hospitals aided by researchers monitored and tracked tobacco use; those not aligned with researchers did not. The wording of tobacco items most commonly included use within the last 6-months (42%), 30-days (39%), or 7-days (33%), or use without reference to time (e.g., "Do you smoke?"; 39%); wording sometimes depended on admitting form space limitations. The admission process determined where the tobacco item appeared, which differed by hospital-75% included it on an admitting form (75%) and/or nursing assessment (56%); the item sometimes varied by unit. There were also different processes by which the item triggered delivery of cessation interventions; most frequently (69%), staff nurses were triggered to provide an intervention. The findings suggest that adding a tobacco use question to a hospital's admitting process is potentially not that simple. Deciding on the purpose of the question, when it will be asked and by whom, space allotted on the form, and how it will trigger an intervention are important considerations that can affect the question wording, form/location, systems required, data extraction, and resources.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".