Social media and postsecondary student adoption of mental health labels: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Many postsecondary students use social media at an age when mental health issues often arise for the first time. On social media, students describe their mental states or social interactions using psychiatric language. This is a process of mental health labelling as opposed to receiving a formal diagnosis from a psychiatrist. Despite substantial literature on psychiatric labelling effects such as stigma, little research has addressed the mechanisms and effects of labelling through social media. Our objective is to summarise the existing evidence to address this gap. METHODS AND ANALYSIS: This review includes articles in English published since 1995 on how postsecondary students interact with mental health labels in their use of social media. We will consider empirical studies and theses. The search strategy includes SCOPUS, PubMed, OVID MEDLINE (to access APA PsycINFO), Web of Science and ProQuest Global Dissertations and Theses. This scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for protocols and Scoping Reviews guidelines. The artificial intelligence application, Connected Papers, will assist in identifying additional references. The outcomes of interest are labelling by self or others and changes in self-concept and presentation associated with these labels. Two researchers will independently identify the included studies and extract data, solving disagreements with a third opinion. We will produce tables and narrative descriptions of the operationalisation and measurement methods of labelling and social media use, reported effects and uses of labelling, and explanatory mechanisms for the adoption of labels. ETHICS AND DISSEMINATION: This literature review does not require ethics approval. The researchers will present their findings for publication in an open-access peer-reviewed journal and at student/scientific conferences. Potential knowledge users include university students, social media users, researchers, mental health professionals and on-campus mental health services.
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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 | 0.099 | 0.106 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.098 | 0.018 |
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".