A Research Protocol to Test the Effectiveness of Text Messaging and Reminder Calls to Increase Service Use Referrals in a Community Engagement Program
Bibliographic record
Abstract
BACKGROUND: Mobile phoned-based interventions have been increasingly used in clinical populations to improve health and health care delivery. The literature has shown that mobile phone-based text messages (short message service, SMS) are instantaneous, cost effective, and have less chance of being misplaced. Studies using mobile phone based-text messages have reported text messages as effective reminders that have resulted in increased appointment attendance, adherence to treatment, and better self-management. There have been no reports of adverse events when using text messaging in terms of misreading or misinterpreting data, transmitting inaccurate data, losing verbal or nonverbal communication cues, privacy issues, or failure or delay in message delivery. However, the literature has cited a need for personalized messages that are more responsive to individual needs. In addition, there has been a dearth of information on the use of reminders in nonclinical populations. OBJECTIVE: The goal of this study is to assess the effectiveness of adding reminders in the form of text messaging versus reminder calls versus text messages and reminder calls to increase use of service referrals provided through community outreach. METHODS: A total of 300 participants will be recruited for the study. Each participant will be randomized to one of three arms: a group that receives only reminder calls (CALLSONLY); a group that receives only text message reminders (TEXTONLY); and a group that receives both reminder calls and text messages (CALLS+TEXT). All groups will receive their reminder intervention on the 15th and 45th day after baseline when they receive medical and social service referrals from the community health workers (CHWs). A standard script will be used to administer the call and text reminders and a 15-item telephone-based satisfaction survey will be administered to assess the participant satisfaction with the process of receiving periodic reminders. RESULTS: The study is in the recruitment and follow-up phase. The authors anticipate completion of recruitment, interventions, and data entry by July 2016. Preliminary results are expected to be available by September 2016. CONCLUSIONS: This study will provide an opportunity to test the effectiveness of mobile-based interventions on nonclinical, community-recruited populations. In particular, such a protocol would increase the effectiveness of a community-based engagement program by instating a formal reminder system for all program members who receive social and/or medical service referrals during outreach in the community. Findings from this study would guide the development and implementation of reminder protocols for community-based engagement programs nationwide.
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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 | 0.069 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.015 |
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".