Text Messages in the Field of Mental Health: Rapid Review of the Reviews
Bibliographic record
Abstract
Background: While mental health problems constitute a worldwide concern contributing to the global rates of morbidity and mortality, conventional mental healthcare services do not meet the current needs. Text messages (TM) represent a live model that incorporates technology into health services, spanning a large number of health conditions and playing different roles that may support the current healthcare system. Objective: To examine the TM services in the field of mental health, regarding their effectiveness, feasibility, acceptability, and economic evaluation in different contexts of mental health diagnoses and during critical times, when provided to individuals with mental health symptoms/disorders. Methods: This rapid review was conducted through an online search in PubMed, Embase, PsycINFO, and Medline databases. The review targeted the review studies which examined online or mobile addiction and mental health services, utilizing TM services. The search was run from the inception up to September 30, 2021. Results: Sixty review articles met the inclusion criteria and were included in this review. All reviews were published over the last decade. The results showed that people of a young age were fairly represented, and most reviews were run over substance use disorders (SUD), including Alcohol. Most reviews examined the effectiveness outcomes of the texting service, while to a lesser extent the acceptability and feasibility, among others. Texting services were reported as effective in psychotic disorders and SUD. However, the results related to depression and anxiety were mixed. Most reviews reported a considerably high risk of bias among their included studies. High satisfaction and acceptability of the texting services were reported for patients with various mental health conditions, including those with severe mental illness. Conclusions: This rapid review highlighted the applications, usability, benefits, and satisfaction with the TM in the field of mental health. For a higher quality of evidence, future studies should consider TM interventions in the contexts with mixed results or a dearth of literature, and during critical times, such as the COVID-19 pandemic. Policy- and decision-makers, therefore, need to further support text-based services with guided investments in interventions that were evidenced to be accepted, economic and feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".