Two-Way Text Messaging to Support Self-Care and Delivery of an Online Sexual Health Service: Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: Digital health care is increasingly used to improve health service accessibility and reduce costs. Remote health care requires a significant self-management role for service users, and this generates information provision and support needs that should be reflected in service planning. SMS text messaging offers a convenient and low-cost method of communication and is increasingly used across digital health care services to provide remote support. OBJECTIVE: The aim of this study was to quantify the number of messages generated through user interaction with a two-way SMS text messaging support service within an online sexual health service and to thematically explore the content of the messages and type of support required to facilitate self-management. METHODS: The content of all SMS text messages received by an online sexual health service was analyzed from April 4, 2018, to July 5, 2018. Messages were classified as being either administrative or clinical in nature and service or user initiated. For those messages that were both clinical and user initiated, a qualitative thematic analysis was completed to fully describe the content of the interactions. RESULTS: A total of 267 actionable messages were generated per 1000 orders requested through the service. Of the 8562 messages, 5447 (63.62%) messages were administrative and 3115 (36.38%) were clinical. Overall, 4306 of the 8562 messages (50.29%) responded to service-generated queries reflecting the public health and clinical responsibilities of an online provider, and 4256 (49.71%) were user-generated queries, demonstrating a willingness by users to proactively engage with a two-way SMS text messaging support service. Of the 3115 clinical messages, 968 (31.08%) clinical messages were user initiated and shared personal and complex clinical information, including requests for help with the self-testing process and personalized clinical advice relating to symptoms and treatment. CONCLUSIONS: This study demonstrates the willingness of users of an online sexual health service to engage with two-way SMS text messaging and provides insight into the quantity and nature of the support required to facilitate service delivery and self-care. Further work is required to understand the range of clinical problems that can be managed within this medium.
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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 | 0.097 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".