Social media and mobile health technology for cancer screening: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Cancer is one of the leading causes of death globally and many jurisdictions have developed population-based cancer screening programmes to reduce the public health burden of disease. However, screening participation remains suboptimal. Social media and other mobile health (mHealth) technologies are increasingly being used for health promotion and behaviour change. This paper reports on the protocol for a systematic review exploring the effect of social media and other mHealth interventions on cancer screening participation and intention. METHODS AND ANALYSIS: This protocol is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist. We will include any randomised controlled trials or quasi-experimental studies with a pre/post design conducted in adults ≥18 years of age that report on the effectiveness of a social media or mHealth intervention on screening participation or intention (inclusive of breast, cervical, colorectal, prostate and lung cancer). Interventions will be inclusive of those delivered online or through a computer using an established social media platform or a new purpose-built platform, or those delivered through cellphones or other wireless technologies. Any comparator will be acceptable (control group, alternate intervention or pre/post design). We will search Medline, EMBASE, PsycINFO, Scopus, CINAHL, the Cochrane Central Register of Controlled Trials, and Communication and Mass Media Complete from 1 January 2000 to 31 May 2019. Two independent reviewers will screen titles, abstracts and full-text articles with conflicts resolved through discussion or by a third reviewer, as needed. The two reviewers will also independently complete risk of bias assessments for each included study. We will report on the characteristics of the studies, participants and interventions in descriptive narrative form and report the absolute and relative differences in screening and intention attributable to social media and mobile technology interventions. ETHICS AND DISSEMINATION: As this is a systematic review, ethical approval for conduct of this study is not required. We will pursue publication of study results in a relevant peer-reviewed journal and report our findings according to the PRISMA checklist. TRIAL REGISTRATION NUMBER: CRD42019139615.
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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 | 0.096 | 0.131 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.023 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.115 | 0.012 |
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".