Striving for evidence-based health care with eHealth and technology in a time of half-truths and disinformation
Notice bibliographique
Résumé
October 20, 2021, marks World Evidence-Based Healthcare Day with the theme of “The Role of Evidence in an Infodemic.” In the current climate of COVID-19, “fake news,” and heightened public distrust, this theme is astute and judicious. We are experiencing an infodemic given the overabundance of both accurate and false COVID-19 information that is spreading rapidly via digital (eg, social media) platforms, making it problematic for people to find trustworthy information.1 In a worldwide pandemic, it is essential to have access to dependable, evidence-informed health care; however, this is a challenge because evidence is often emerging in real time, and recommendations are quickly changing and sometimes embedded in politics. Evidence-based information during a pandemic is critical yet compromised due to the spread of disinformation, which is the strategic and deliberate spread of false information.2 There is also the battle with misinformation, which is the spread of false information circulated without intent.2 One area where there is an overabundance of information circulating is the safety of the COVID-19 vaccine during pregnancy. It is well-established that pregnant people are at an increased risk of serious adverse outcomes if infected with COVID-19, including preeclampsia, preterm birth, and stillbirth.3 Pregnant people with COVID-19 are also more likely to have a cesarean delivery and be admitted to intensive care, which is associated with poorer prognosis.4 Despite these risks to themselves and their fetus, and despite the recommendation by the American College of Obstetricians and Gynecologists5 that the vaccine is effective and safe during pregnancy,6 some pregnant people are still hesitant to obtain the COVID-19 vaccine. This is not overly surprising given that initial vaccine trials excluded pregnant people; however, this has since been rectified with solid evidence about the safety and effectiveness in this vulnerable population.6 Despite this evidence and research suggesting pregnant people are at increased risk of adverse outcomes from COVID-19, there is both disinformation and misinformation readily available online suggesting the vaccinations are not safe. This is not a new challenge for pregnant people; vaccine hesitancy and inaccurate information existed prior to the COVID-19 pandemic.7 Fortunately, there are social media campaigns available attempting to provide trustworthy information to this population. One is the Pandemic Pregnancy Guide, led by Canadian obstetric providers at St. Michael's Hospital in Toronto, Ontario, Canada, which shares relevant evidence-based information in an easy-to-read format on Instagram and Twitter; they also host podcasts with medical experts to answer current pressing questions. The use of social media and other forms of technology to combat vaccine misinformation during the infodemic is just one example of how technology can improve health care. Using technology to disseminate evidence-based information and facilitate health care practice is not new and has been used across a variety of health concerns and conditions. In this issue of JBI Evidence Synthesis alone, there are four reviews addressing the use of health technologies or eHealth interventions to improve care, and six protocols detailing review projects in a variety of populations using technologies such as mobile health (mHealth),8,9 three-dimensional printing,10 technology-based interventions,11,12 and wearable technology.13 A systematic review by Nick et al.14 found that the use of telemonitoring significantly improved short-term self-care behaviors among community-dwelling adults with heart failure, while in a scoping review by Cooper et al.,15 56% of included studies reported outcomes relating to clinical effectiveness for the use of health technologies to prevent or detect falls in hospital inpatients. In Macdonald et al.'s16 scoping review, several assistive technologies to support social interaction in long-term care residents were identified; however, there are important barriers, including health care provider workload and residents’ ability to actually use the technological devices. Johnsen et al.'s17 scoping review identified a gap in the use of eHealth interventions to facilitate work participation, which suggests an area for future research. Although all of these reviews were initiated prior to the emergence of COVID-19 and the rise of the infodemic, it is clear that eHealth and health technology are now deeply embedded in our lives both as patients and in the provision of care as practitioners. To counter the infodemic, Eysenbach18 outlines four management pillars: i) information monitoring, ii) building eHealth and science literacy capacity, iii) encouraging fact checking and peer-review, and iv) accurate and timely knowledge translation. While the COVID-19 pandemic heightened this challenge, addressing disinformation and misinformation is important for all individuals and across health issues. Access to evidence-based information using eHealth and technology innovation is rapidly increasing and is having a positive impact, as evident by the reviews in this issue. Moving forward, it will be important to continue to identify new ways to ensure knowledge translation of evidence-based information to the public to counter false information and to improve science literacy in the public. The combined approach of eHealth interventions with light-touch human contact is promising to encourage not only intervention uptake and engagement, but also to address concerns and questions from individuals accessing evidence-based information who may have encountered a negative spin on the evidence.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».