Social Media for e-Government in the Public Health Sector: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Public sector organizations worldwide are engaging with social media as part of a growing e-government agenda. These include government departments of health, public health agencies, and state-funded health care and research organizations. Although examples of social media in health have been described in the literature, little is known about their overall scope or how they are achieving the objectives of e-government. A systematic literature review is underway to capture and synthesize existing evidence on the adoption, use, and impacts of social media in the public health sector. A series of parallel scoping exercises has taken place to examine (1) relevant existing systematic reviews, to assess their focus, breadth, and fit with our review topic, (2) existing concepts related to e-government, public health, and the public health sector, to assess how semantic complexity might influence the review process, and (3) the results of pilot searches, to examine the fit of social media within the e-government and health literatures. The methods and observations of the scoping exercises are reported in this protocol, alongside the methods and interim results for the systematic review itself. OBJECTIVE: The systematic review has three main objectives: To capture the corpus of published studies on the uses of social media by public health organizations; to classify the objectives for which social media have been deployed in these contexts and the methods used; and to analyze and synthesize evidence of the uptake, use, and impacts of social media on various outcomes. METHODS: A set of scoping exercises were undertaken, to inform the search strategy and analytic framework. Searches have been carried out in MEDLINE, the Cochrane Library, Web of Science, and the Scopus international electronic databases, and appropriate gray literature sources. Articles published between January 1, 2004, and July 12, 2015, were included. There was no restriction by language. One reviewer (AT) has independently screened citations generated by the search terms and is extracting data from the selected articles. A second author (CP) is cross-checking the outputs to ensure the fit of selected articles with the inclusion criteria and appropriate data extraction. A PRISMA flow diagram will be created, to track the study selection process and ensure transparency and replicability of the review. RESULTS: Scoping work revealed that the literature on social media for e-government in the public health sector is complicated by heterogeneous terminologies and concepts, although studies at the intersection of these three topics exist. Not all types of e-government are evident in the health care literature. Interim results suggest that most relevant articles focus on usage alone. CONCLUSIONS: Public health organizations may be taking it for granted that social media deliver benefits, rather than attempting to evaluate their adoption or impacts. Published taxonomies of e-government hold promise for organizing and interpreting the review results. The systematic review is underway and completion is expected in the beginning of 2016. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews: CRD42015024731; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015024731 (Archived by WebCite at http://www.webcitation.org/6dV1Cin91).
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 | 0.159 | 0.188 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 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".