ADDRESSING BARRIERS IN NON-VERBAL COMMUNICATION DURING TELECONSULTATION IN THE ERA OF COVID-19. SYSTEMATIC REVIEW
Notice bibliographique
Résumé
ADDRESSING BARRIERS IN NON-VERBAL COMMUNICATION DURING TELECONSULTATION IN THE ERA OF COVID-19. SYSTEMATIC REVIEW Question: ¿What are the barries of non-verbal communication in telemedicine? Introduction: Since the COVID 19 pandemic began, the use of technology has become more relevant in the health sector. Technical aspects are currently being reformed to ensure greater equity in the provision of health services. Despite the progress made, it is difficult to equate it with an in-person consultation; due to the loss of non-verbal communication skills and the lack of strategies to improve it Searching: From the beginning of time until August 2020. The following electronic bibliographic databases will be searched to identify relevant studies: MEDLINE, PubMed, Ovid, APA, EBSCO, Web Of Science, Scielo. No language restrictions will be applied. All racial groups will be included In addition, a manual search will be carried out to supplement the electronic search, and the reference lists of relevant studies will also be screened for any further material for inclusion. Selection criteria:All the patients that have been attended through telemedicine in aspects that include promotion, prevention, treatment and rehabilitation attentions by any health workers will be included. All studies that focus on non-verbal communication in telemedicine and the barriers identified Will be included cross-sectional, retrospective and prospective cohorts and cases series. Will be excluded review articles clinical trials, abstracts of meeting, case reports, letters, editorials, and systematic reviews. Principal outcomes The primary outcome of interest in this systematic review is to describe and categorize the barriers of non-verbal communication in telemedicine. Additional outcomes Satisfaction, treatment, adherence, clinical improvement and strategies used in patients attended through telemedicine Data extraction Two reviewers/authors (GOC and GVE) will assess the eligibility of the studies retrieved during the searches independently using the inclusion and exclusion criteria. The following data will be extracted from the studies selected and recorded in the Excel file: first author and year, study design, sample, intervention, studies groups, and outcomes. The results will be checked by two reviewers (GOC and GVE) and disagreements will be resolved by a third reviewer (PTI). We may also contact the original authors for additional relevant information. Quality assessment A reviewers/authors (PTI) will independently evaluate the quality of the trials through assessing the risk of bias using the following tools, when appropriate: For observational study, we will use the Newcastle-Ottawa Scale and The Murat tool's for clinical cases. Any disagreements will be resolved through discussions between these two reviewers/authors (GOC and GVE), involving at least one additional reviewer/author (PTI) until the consensus is achieved. We will illustrate the potential biases within each of the included studies by presenting a ’risk of bias’ table, graph and summary. Synthesis of information. This study is a systematic review without meta-analysis. All information collected will be categorized on basis on non-verbal communication dimensions proposed. key words: Telemedicine, Non-verbal communication, barriers
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 | 0,013 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,007 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 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 ».