ADDRESSING BARRIERS IN NON-VERBAL COMMUNICATION DURING TELECONSULTATION IN THE ERA OF COVID-19. SYSTEMATIC REVIEW
Bibliographic record
Abstract
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
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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".