Social Media for e-Government in the Public Health Sector: Protocol for a Systematic Review
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,159 | 0,188 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,008 |
| Méta-épidémiologie (sens large) | 0,015 | 0,015 |
| Bibliométrie | 0,018 | 0,018 |
| Études des sciences et des technologies | 0,006 | 0,009 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,005 | 0,007 |
| Intégrité de la recherche | 0,010 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,065 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».