Fidelity monitoring across the seven studies in the Consortium of Hospitals Advancing Research on Tobacco (CHART)
Bibliographic record
Abstract
BACKGROUND: This paper describes fidelity monitoring (treatment differentiation, training, delivery, receipt and enactment) across the seven National Institutes of Health-supported Consortium of Hospitals Advancing Research on Tobacco (CHART) studies. The objectives of the study were to describe approaches to monitoring fidelity including treatment differentiation (lack of crossover), provider training, provider delivery of treatment, patient receipt of treatment, and patient enactment (behavior) and provide examples of application of these principles. METHODS: Conducted between 2010 and 2014 and collectively enrolling over 9500 inpatient cigarette smokers, the CHART studies tested different smoking cessation interventions (counseling, medications, and follow-up calls) shown to be efficacious in Cochrane Collaborative Reviews. The CHART studies compared their unique treatment arm(s) to usual care, used common core measures at baseline and 6-month follow-up, but varied in their approaches to monitoring the fidelity with which the interventions were implemented. RESULTS: Treatment differentiation strategies included the use of a quasi-experimental design and monitoring of both the intervention and control group. Almost all of the studies had extensive training for personnel and used a checklist to monitor the intervention components, but the items on these checklists varied widely and were based on unique aspects of the interventions, US Public Health Service and Joint Commission smoking cessation standards, or counselor rapport. Delivery of medications ranged from 31 to 100 % across the studies, with higher levels from studies that gave away free medications and lower levels from studies that sought to obtain prescriptions for the patient in real world systems. Treatment delivery was highest among those studies that used automated (interactive voice response and website) systems, but this did not automatically translate into treatment receipt and enactment. Some studies measured treatment enactment in two ways (e.g., counselor or automated system report versus patient report) showing concurrence or discordance between the two measures. CONCLUSION: While fidelity monitoring can be challenging especially in dissemination trials, the seven CHART studies used a variety of methods to enhance fidelity with consideration for feasibility and sustainability. TRIAL REGISTRATION: Dissemination of Tobacco Tactics for hospitalized smokers. Clinical Trials Registration No. NCT01309217.Smoking cessation in hospitalized smokers. Clinical Trials Registration No. NCT01289275.Using "warm handoffs" to link hospitalized smokers with tobacco treatment after discharge: study protocol of a randomized controlled trial. Clinical Trials Registration No. NCT01305928.Web-based smoking cessation intervention that transitions from inpatient to outpatient. Clinical Trials Registration No. NCT01277250.Effectiveness of smoking-cessation interventions for urban hospital patients. Clinical Trials Registration No. NCT01363245.Comparative effectiveness of post-discharge interventions for hospitalized smokers. Clinical Trials Registration No. NCT01177176.Health and economic effects from linking bedside and outpatient tobacco cessation services for hospitalized smokers in two large hospitals. Clinical Trials Registration No. NCT01236079.
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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 | 0.332 | 0.505 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".