www.mdpi.com/journal/ijerph It’s Not That Simple: Tobacco Use Identification and Documentation in Acute Care
Bibliographic record
Abstract
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
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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".