Considering the Impact of Social Media on Contemporary Improvement of Australian Aboriginal Health: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Social media may have a significant role in influencing the present and future health implications among Australian Aboriginal and Torres Strait Islander people, yet there has been no review of the role of social media in improving health. OBJECTIVE: This study aims to examine the extent of health initiatives using social media that aimed to improve the health of Australian Aboriginal communities. METHODS: A scoping review was conducted by systematically searching databases CINAHL Plus; PubMed; Scopus; Web of Science, and Ovid MEDLINE in June 2017 using the terms and their synonyms "Aboriginal" and "Social media." In addition, reference lists of included studies and the Indigenous HealthInfonet gray literature were searched. Key information about the social media intervention and its impacts on health were extracted and data synthesized using narrative summaries. RESULTS: Five papers met inclusion criteria. All included studies were published in the past 5 years and involved urban, rural, and remote Aboriginal or Torres Strait Islander people aged 12-60 years. No studies reported objective impacts on health. Three papers found that social media provided greater space for sharing health messages in a 2-way exchange. The negative portrayal of Aboriginal people and negative health impacts of social media were described in 2 papers. CONCLUSIONS: Social media may be a useful strategy to provide health messages and sharing of content among Aboriginal people, but objective impacts on health remain unknown. More research is necessary on social media as a way to connect, communicate, and improve Aboriginal health with particular emphasis on community control, self-empowerment, and decolonization.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".