Online Social Networking among Clinically Depressed Young People: Scoping Review of Potentially Supportive or Harmful Behaviors
Bibliographic record
Abstract
Online social networking sites are ubiquitous and prevalently used by young people. The COVID-19 pandemic demonstrated the potential for such sites to bring isolated people together to support their mental health. Virtual communications, however, are not without risks. Substantial knowledge exists on attendant risks and protections among the general population, but much less seems known about their effects among clinical populations. This scoping review mapped the novel knowledge and knowledge gaps related to online social networking experiences and perceptions of depressed young people, adolescents to emergent adults. It also explored moderators of their social networking supports versus harms. A broad search of published and gray research literature between 2010 and 2021 found seven intensive interview studies, three surveys and a brief prospective cohort. Their aggregate sample of 915 clinically depressed young people was most typically, outpatient adolescents in the USA. They also prevalently used online social networks, but their perceptions seemed more positive than those of their peers without a diagnosis of depression. In fact, their positive perceptions (60%; e.g., ease of access and communication with providers, support from positive peers) were nearly two-fold greater than their negative perceptions. (36%; e.g., self-denigrating comparisons with “friends,” cyberbullies). Tentatively suggested moderators of risks versus protections were found at the intersection of gender, ethnicity and socioeconomic status. However, given this relatively new field’s limits, these are probably best thought of as screened hypotheses for future full systematic review development and primary research testing. Clinical implications are discussed.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".