The Computer-Assisted Brief Intervention for Tobacco (CABIT) Program: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Health care providers do not routinely carry out brief counseling for tobacco cessation despite the evidence for its effectiveness. For this intervention to be routinely used, it must be brief, be convenient, require little investment of resources, require little specialized training, and be perceived as efficacious by providers. Technological advances hold much potential for addressing the barriers preventing the integration of brief interventions for tobacco cessation into the health care setting. OBJECTIVE: This paper describes the development and initial evaluation of the Computer-Assisted Brief Intervention for Tobacco (CABIT) program, a web-based, multimedia tobacco intervention for use in opportunistic settings. METHODS: The CABIT uses a self-administered, computerized assessment to produce personalized health care provider and patient reports, and cue a stage-matched video intervention. Respondents interested in changing their tobacco use are offered a faxed referral to a "best matched" tobacco treatment provider (ie, dynamic referral). During 2008, the CABIT program was evaluated in an emergency department, an employee assistance program, and a tobacco dependence program in New Jersey. Participants and health care providers completed semistructured interviews and satisfaction ratings of the assessment, reports, video intervention, and referrals using a 5-point scale. RESULTS: Mean patient satisfaction scores (n = 67) for all domains ranged from 4.00 (Good) to 5.00 (Excellent; Mean = 4.48). Health care providers completed satisfaction forms for 39 patients. Of these 39 patients, 34 (87%) received tobacco resources and referrals they would not have received under standard care. Of the 45 participants offered a dynamic referral, 28 (62%) accepted. CONCLUSIONS: The CABIT program provided a user-friendly, desirable service for tobacco users and their health care providers. Further development and clinical trial testing is warranted to establish its effectiveness in promoting treatment engagement and tobacco cessation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".