Feasibility of a Proactive Text Messaging Intervention for Smokers in Community Health Centers: Pilot Study
Bibliographic record
Abstract
Introduction: Few smokers receive evidence-based cessation services during primary care visits. We aimed to assess the feasibility of a proactive text messaging program for primary care patients who smoke. Methods: We used electronic health records (EHRs) to identify smokers from two Massachusetts community health centers who had a mobile phone number listed. In March 2014-June 2015 patients were screened by their primary care physician then sent a proactive text message inviting them to enroll by texting back. Patients who opted-in were asked about their readiness to quit. The text message program included messages from the QuitNowTXT library and novel content for smokers who were not ready to quit. Results: Among 949 eligible smokers, 88 (9%) enrolled after receiving a single proactive text message. Compared with those who did not enroll, enrollees were more often female (61% vs. 48%, p=0.02) but otherwise did not differ in age, race, insurance status, or comorbidities. Twenty-eight percent of enrollees were not ready to quit in the next 30 days. The median time in the program was 9 days (interquartile range 2-32). Twenty-five percent of current smokers sent one or more keyword requests to the server. These did not differ by readiness to quit. Conclusions: A proactively delivered text messaging program targeting primary care patients who smoke was feasible and engaged both smokers ready to quit and those not ready to quit. This method shows promise as part of a population health model for addressing tobacco use outside of the primary care office.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".