The Stanford Brainstorm Social Media Safety Plan (SMS): Introducing a New Tool
Bibliographic record
Abstract
Unlabelled: We propose the Stanford Brainstorm Social Media Safety Plan (SMS) as a user-friendly, collaborative, and effective tool to mitigate the imminent dangers and risks to mental health that are associated with social media use by children, adolescents, and young adults. This tool is informed and inspired by suicide safety plans as part of suicide safety planning, which have long shaped the standard of care for psychiatric discharges from inpatient units, emergency rooms, and comprehensive psychiatric emergency programs, as well as longitudinal outpatient care following occurrences of suicidal ideation or suicide attempts. In many systems including those of the Veterans Health Administration, they constitute an absolute requirement prior to the discharge of the patient. This social media safety plan is to be used proactively, in times of normalcy as well as crisis. While there are parental controls for digital devices and online platforms, official legal age requirements for online accounts, and individual parenting approaches, there is a dearth of practical tools that youth, families, schools, and communities can use to shape and alter social media use parameters, rules, and habits. Furthermore, providers in psychiatry, child and adolescent psychiatry, and mental health at large are often confronted with behaviors and issues related to social media use during time- and resource-limited appointments, providing a massive opportunity for interventions that are harm reduction-oriented and easy to disseminate. While it has not been studied in a clinical trial, we have used it extensively with patients and families, and presented it to larger audiences at mental health and technology conferences over the past two years. The responses and feedback we have received, as well as reported anecdotal experiences with using it, have been overwhelmingly positive. An already unfolding child and adolescent mental health epidemic in the United States has been observed and deepened partly by way of easy access to social media (and digital-screen time) with inadequate safeguards and monitoring in place. Social media's impacts and related interventions require a multitiered biopsychosocial and cultural approach: at the level of the individual child, the family, the school, the state, the market, and the nation. At the level of youth and their parents or caregivers, practical tools are desperately needed. We propose the SMS as one such significant tool.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".