Skip to content
Independent researchSIGNAL & SELF
Menu

Investigate

AlgorithmsSelf-diagnosisIdentityReinforcementSocial contagionCOVID eraDiagnostic trends

Cases

Functional tic-like behavioursDissociative identity disorder

Evidence

Evidence explorerResearch libraryHypothesesResearch gapsHow we knowReferences

Paper

Paper
PathwaysAbout

Evidence

Claims, not vibes

Filter by evidence level, causality, and condition. Commentary can appear among sources; it is never promoted to proof.

Established. Convergent evidence across methods; remaining debate is about details, not existence.

Strong evidence. High-quality reviews or large, careful studies support the claim, with residual limits.

Moderate evidence. Credible studies exist, but designs, measures, or consistency leave substantial uncertainty.

Emerging. Recent or limited studies point in a direction that still needs replication.

Association. Variables move together. Cause has not been shown.

Clinical observation. Clinicians have documented a pattern. Population evidence is thinner.

Case series. A defined set of cases, usually from specialist clinics, without a robust denominator.

Mechanistically plausible. Fits known mechanisms; direct tests of this claim are missing or indirect.

Contested. Competent literatures disagree, often along theoretical lines.

Contradictory. Findings point in incompatible directions.

Unknown. The honest current answer is that we do not know.

21 CLAIMS

Claim

Overall time spent on digital technology or social media shows only small average associations with adolescent well-being.

Evidence level
Strong evidence
Causality status
associational
Population
General adolescent populations in high-income countries
Age range
Adolescents
Platform
general-social-media
Condition
general-mental-health
Effect where available
Typically well under 1% of variance in well-being in specification-curve work
Limitations
Well-being ≠ clinical disorder; time-use ≠ recommended content; averages hide person-specific effects.
Alternative explanations
  • Reverse causation: distressed youth use platforms differently
  • Confounding by sleep, family, socioeconomic status, or pre-existing conditions
  • Measurement error in both exposure and outcome
Unanswered questions
  • Do small average effects conceal clinically important effects in susceptible subgroups?
  • Does content type dominate time?

Supporting research

  • Orben, A. & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use.
  • Odgers, C. L. & Jensen, M. R. (2020). Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions.
  • Valkenburg, P. M., Meier, A. & Beyens, I. (2022). Social media use and its impact on adolescent mental health: An umbrella review of the evidence.

Conflicting / complicating research

  • Twenge, J. M., Joiner, T. E., Rogers, M. L. & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time.
  • Riehm, K. E. et al. (2019). Associations between time spent using social media and internalizing and externalizing problems among US youth.