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Record W2790289841 · doi:10.2196/mental.7957

Targeted Secure Messages to Facilitate Access to Tobacco Treatment Counseling for Veterans: Feasibility Study

2018· article· en· W2790289841 on OpenAlexvenueno aff
Shaun Shahani, Pearl Korenblit, Pauline Thomas, Marian R. Passannante, Richard D. Carr, Lynn Davis

Bibliographic record

VenueJMIR Mental Health · 2018
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePasswordTobacco useSmoking cessationSmokeless tobaccoShort Message ServiceFamily medicineComputer securityComputer scienceEnvironmental healthTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Studies show that combining nicotine replacement therapy (NRT) with tobacco treatment counseling is most effective for smoking cessation. However, tobacco treatment counseling has been underutilized across the nation. A secure email message sent to patients already taking NRT was hypothesized to increase the utilization of tobacco treatment counseling among Veterans in New Jersey. Secure messaging for communication between patients and providers was implemented through a web-based password-protected, secure messaging account, where veterans get notified through their personal email when they have a message awaiting them. OBJECTIVE: The main objective of this project was to determine if there was a significant increase in adoption of tobacco treatment counseling among Veterans who received a secure message describing the options for tobacco treatment counseling available to them. Secondary objectives were to demographically characterize Veterans who were and were not enrolled in secure messaging, as well as those who opened or did not open a message. Finally, because the language and content of the messages were changed across project phases, this project also sought to determine (by analysis of response rates) the type of language that was most effective at eliciting a response. METHODS: Over two phases, messages were sent to two samples of Veterans prescribed NRT within the prior 90 days of each phase. In phase 1, one message was sent in December 2015 (message 1). In phase 2, one message was sent in July 2016 (message 2) and the same message (message 3) was resent in August 2016 to persons who did not open message 2. Messages 2 and 3 were more directive than message 1. Response rates to message 1 versus message 2 were compared. A logistic regression analysis determined effect of age and gender on enrollment in secure messaging across both phases. The effectiveness of each phase at increasing tobacco treatment counseling was analyzed using a McNemar test. RESULTS: Message 2, sent to 423 Veterans, had a significantly higher response rate than message 1, sent to 348 Veterans (18%, 17/93 vs 8%, 6/78, P=.04). Phase 2 (ie, messages 2 and 3) significantly increased utilization of tobacco treatment counseling (net increase of six tobacco treatment counseling adopters, P=.04), whereas phase 1 (ie, message 1) did not (net increase of two tobacco treatment counseling adopters, P=.48). Women (odds ratio [OR] 1.6, 95% CI 1.1-2.3) and those aged 30 to 49 years (compared to other age groups) were more likely to be enrolled in secure messaging. Gender and age were not significant predictors of opening or replying to either message. CONCLUSIONS: Although the effect was small, secure messaging was a useful modality to increase tobacco treatment counseling. Directive content with a follow-up message appeared useful. Female Veterans and/or Veterans aged between 30 and 49 years are more likely to use secure messaging.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.145
GPT teacher head0.453
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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