Celebrities’ impact on health-related knowledge, attitudes, behaviors, and status outcomes: protocol for a systematic review, meta-analysis, and meta-regression analysis
Bibliographic record
Abstract
BACKGROUND: Celebrities are highly influential people whose actions and decisions are watched and often emulated by wide audiences. Many celebrities have used their prominent social standing to offer medical advice or endorse health products, a trend that is expected to increase. However, the extent of the impact that celebrities have in shaping the public's health-related knowledge, attitudes, behaviors, and status is unclear. This systematic review seeks to answer the following questions: (1) Which health-related outcomes are influenced by celebrities? (2) How large of an impact do celebrities actually have on these health-related outcomes? (3) Under what circumstances do celebrities produce either beneficial or harmful impacts? METHODS: Ten databases were searched, including MEDLINE, EMBASE, PsycINFO, PubMed, CINAHL, Communication Complete, Sociological Abstracts, Social Sciences Citation Index, Journals @ Scholars Portal, and ProQuest Dissertations & Theses A&I. Two reviewers conducted title and abstract screening and full-text screening to identify primary studies that employed empirical methods (either quantitative or qualitative) to examine celebrities' impact on health-related knowledge, attitudes, behaviors, or status outcomes. DISCUSSION: The results of this review will contribute to our understanding of celebrity influences and how to design positive evidence-based celebrity health promotion activities. In addition, these findings can help inform the development of media reporting guidelines pertaining to celebrity health news and provide guidance to public health authorities on whether and how to respond to or work with celebrities. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015019268.
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 | 0.094 | 0.145 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.029 | 0.036 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".