Bibliographic record
Abstract
The relationship between social media use and mental health has been well documented. A study by Bradley et al. (2023) examined whether reducing social media use to 30 minutes per day would improve mental health, but found no significant improvements, partly due to participants’ lack of adherence to study instructions. The current study examines qualitative responses to an open-ended reflection question from participants of this previous study. Most participants struggled to limit their social media use, largely because of benefits social media use provided and perceived consequences of not using social media. Many participants wanted to limit their social media use, despite most failing to do so during the study. Participants who did limit their social media use experienced improved mental health when limiting. Additionally, many participants had negative opinions about social media use, such as increasing social comparison, though positive and mixed opinions were also reported. This study suggests social media use limiting interventions may not be viable, and that different types of social media use must also be considered. Additionally, research viewing social media use as only having negative impacts is inaccurate; this study demonstrates that there is a continuum of effects it may have.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".