Videoconferencing interventions and COPD patient outcomes: A systematic review
Bibliographic record
Abstract
IntroductionVideoconferencing circumvents various physical and financial barriers associated with in-person care. Given this technology's potential benefits and timely nature, we conducted a systematic review to understand how videoconferencing for chronic obstructive pulmonary disease (COPD) follow-up care affects patient-related outcomes.MethodsWe included primary research evaluating the use of bidirectional videoconferencing for COPD patient follow-up. The outcomes of interest were resource utilization, mortality, lifestyle factors, patient satisfaction, barriers, and feasibility. We searched MEDLINE, EMBASE, EBM Reviews, and CINAHL databases for articles published from January 1, 2010, to August 2, 2021. Relevant information was extracted and presented descriptively and common themes and patterns were identified. The risk of bias for each study was assessed using design-specific validated tools.ResultsWe included 39 studies of 18,194 patients (22 quantitative, 12 qualitative, and 5 mixed methods). The included studies were grouped by type of intervention; 18 studies explored videoconferencing for exercise, 19 explored videoconferencing for clinical assessment/monitoring, and 2 examined videoconferencing for education. Generally, videoconferencing was associated with high levels of patient satisfaction. There were mixed results in terms of its effects on resource utilization and lifestyle-related factors. Additionally, 12 studies were at high risk of bias, indicating that these results should be interpreted with caution.ConclusionsThe videoconferencing interventions resulted in high levels of patient satisfaction, despite facing technological issues. Overall, more research is needed to better understand the effects of videoconferencing interventions on resource utilization and other patient outcomes, quantifying their advantages over in-person care.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".