Attention to Instagram features in female youth with anxiety symptoms
Bibliographic record
Abstract
Social media's highly visual and interactive nature fosters social comparisons and can thereby exacerbate feelings of inadequacy, especially among youth with anxiety. Compared to their non-anxious peers, anxious youth are more likely to use social media and may be more susceptible to engaging in social comparison, as suggested by prior literature. Anxious youth are also more attentive to indicators of social status and threats. Thus, this study explored the attention bias mechanisms underlying anxiety in response to social media status cues (e.g., follower counts, likes). We predicted that with increasing anxiety symptoms, youth would avoid allocating their attention to these cues. We recruited 69 shy or anxious young females with anxiety symptoms to view Instagram profiles [ M age = 20.44 years, SD age = 1.84 years]. We recorded participants' eye movements with a high degree of spatial and temporal resolution while participants freely engaged with these profiles. Results showed that with increasing anxiety symptoms, youths' first fixation latency was significantly longer and their fixation duration shorter for social media status cues compared to the overall profile, representing an attentional avoidance pattern. This pattern was observed for both popular and less-popular profiles. These findings add to evidence that anxiety symptoms are linked to differences in visual attention to social media status cues. Further research is needed to examine these effects across genders, platforms, and in relation to other psychological constructs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".