Health Care Professionals’ Experiences With the Use of Video Consultation: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The number of remote video consultations between doctors and patients has increased during the last few years and especially during the COVID-19 pandemic. The health care service is faced with rising rates of chronic illness and many patients who are more confident in self-management of their illnesses. In addition, there is an improved long-term outlook for serious conditions, such as cancer, that might require flexibility in everyday life. OBJECTIVE: This study aimed to investigate how medical doctors in the outpatient clinic use and experience the use of video consultations with hematological patients, with a focus on relational and organizational aspects. METHODS: The study was designed as an explorative and qualitative study. Data were collected via participant observations and focus group interviews with medical doctors. RESULTS: The study identified possibilities and barriers in relation to adapting to the alternative way of meeting patients in the clinical setting. One of the main findings in this study is that the medical doctors were afraid that they missed important observations, as they were not able to perform a physical examination, if needed. They also emphasized that handshake and eye contact were important in order to get an overall impression of the patient's situation. It also became clear that the medical doctors used body language a lot more during video consultation compared with consultation in a physical setting. The medical doctors found the contact with the patients via the screen to be good, and the fact that the technology was working well made them feel comfortable with the video consultation. CONCLUSIONS: In this study, we found that the medical doctors were able to maintain good contact with the patients despite the screen and were able to assess the patients in a satisfying manner. However, there were still uncertainties among some doctors about the fact that they could not examine the patients physically. New knowledge about how to use gestures and body language during video consultation was obtained.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".