Scoping Review of Challenges and Existing Tools
Bibliographic record
Abstract
In light of the COVID-19 public health restrictions, the use of telemedicine has been on the rise. This care delivery model is valued for its potential to increase care access while providing safe care. However, it changes the way patients and providers interact. Communication during video consultations requires embodied engagement to compensate for the physical distance. This study aimed to identify patient-provider communication challenges during video consultations and assess the tools developed to support patient-provider communication according to the published literature from 2019 to 2022. Searches of eight databases (Medline (Ovid), PubMed, ProQuest Nursing and Allied Health, CINAHL, Web of Science, Scopus, PsychInfo, and Social Services Abstracts), and a Google search for grey literature were conducted. Nineteen articles met inclusion criteria. Findings show that patients and providers share the same concerns, such as a lack of trust relating to physical distance, the ability to establish a meaningful relationship, and a lack of confidence in clinical assessment. The available tools, however, are based on guidelines that are difficult to adapt to the diversity of interaction contexts. There is a need for tools that consider the complexity of patient-provider communication in order to address the challenges stemming from the lack of trust in the context of video consultations. These findings can inform strategies for effective patient-provider communication during video consultations to improve the quality of care and optimize outcomes in this context.
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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 | 0.090 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.091 | 0.075 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".