Instagram as a knowledge mobilization platform for youth resilience research: An exploratory study
Bibliographic record
Abstract
Objectives: Social media (SoMe) is globally prevalent, but its relevance for disseminating sensitive topics, such as violence victimization and mental health among adolescents and emerging adults, remain under-researched. Youth-dominate platforms may be well-suited for resilience messaging on safety, health, and well-being, and exploratory knowledge mobilization research. Research from a common team funding source supported a secondary objective that thematically linked research could be used to impact dissemination. Methods: This experiment utilized an ABA design, with a two-week baseline, followed by SoMe posting on weeks "A" and no posting on weeks "B" from a single Instagram account. During posting weeks, image-based messages from nine open access articles, from a risk and resilience research team, were posted three times per day. Each post contained a link to the associated open-access research article. Outcome dissemination indices, collected weekly, were reads of the referenced articles on a research-based networking site, ResearchGate. Results: Instagram indices formed the basis of our manipulation check. Relative to periods of inactivity, periods of active Instagram engagement led to significant increases in the number of Instagram impressions, website clicks, and followers, and in the number of reads of the posted ResearchGate articles. Implications: As the first study to examine Instagram impact for risk and resilience research, these findings encourage further SoMe work in this area of high public health import.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".