Interactive voice response (IVR) for tobacco cessation: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarise the uses, outcomes and implementation of interactive voice response (IVR) as a tobacco cessation intervention. DATA SOURCES: A systematic review was conducted. Searches were performed on 3 May 2023. The strategies used keywords such as "tobacco cessation", "smoking reduction" and "interactive voice recording". Ovid MEDLINE ALL, Embase, APA PsycINFO, CINAHL, Cochrane Library and Web of Science were searched. Grey literature searches were also conducted. STUDY SELECTION: Titles and abstracts were assessed by two independent reviewers. Studies were included if IVR was an intervention for tobacco cessation for adults; any outcomes were reported and study design was comparative. Any abstract included by either reviewer proceeded to full-text review. Full texts were reviewed by two independent reviewers. DATA EXTRACTION: Data were independently extracted by two reviewers using a standardised form. The Risk of Bias Tool for Randomised Trials and the Risk of Bias in Non-Randomised Studies of Interventions tools were used to assess study quality. DATA SYNTHESIS: Of 308 identified abstracts, 20 moderate-quality to low-quality studies were included. IVR was used standalone or adjunctly as a treatment, follow-up or risk-assessment tool across populations including general smokers, hospitalised patients, quitline users, perinatal women, patients with cancer and veteran smokers. Effective studies found that IVR was delivered more frequently with shorter follow-up times. Significant gaps in the literature include a lack of population diversity, limited implementation settings and delivery schedules, and limited patient and provider perspectives. CONCLUSIONS: While the evidence is weak, IVR appears to be a promising intervention for tobacco cessation. However, pilot programmes and research addressing literature gaps are necessary.
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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".