Short message service (SMS) interventions for the prevention and treatment of sexually transmitted infections: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Globally, the incidence of sexually transmitted infections (STI) is rising, posing a challenge to its control and appropriate management. Text messaging has become the most common mode of communication among almost six billion mobile phone users worldwide. Text messaging can be used to remind patients about clinic appointments, to notify patients that it is time for STI re-testing, and to facilitate patient communication with their health professionals with any questions and concerns they may have about their sexual health. While there are a handful of systematic reviews published on short message service (SMS) interventions in a variety of health settings and issues, none are related to sexual health. We plan to conduct a systematic review to examine the impact text messaging might have on interventions for the prevention and care of patients with STIs. METHODS/DESIGN: Eligible studies will include both quantitative and qualitative studies published after 1995 that discuss the efficacy and effectiveness of SMS interventions for STI prevention and management using text messaging. Data will be abstracted independently by two reviewers using a standardized pre-tested data abstraction form. Inter-rater reliability scores will be obtained to ensure consistency in the inclusion and data extraction of studies. Heterogeneity will be assessed using the I2 test and subgroup analyses. A nonhypothesis driven inductive reasoning approach as well as a coding framework will be applied to analyze qualitative studies. A meta-analysis may be conducted if sufficient quantitative studies are found using similar outcomes. DISCUSSION: For this protocol, we identified ten related systematic reviews. The reviews were limited to a particular disease or setting, were not exclusive to SMS interventions, or were out of date. This systematic review will be the first comprehensive examination of studies that discuss the effectiveness of SMS on multiple outcomes that relate to STI prevention and management, covering diverse settings and populations. Findings of the systematic review and any additional meta-analyses will be published and presented to our key knowledge users. This information will provide the evidence that is required to appropriately adopt text messaging into standard practice in STI care.
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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 | 0.090 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.083 | 0.013 |
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".