Enabling Patients as Partners on Virtual Teams: A Scoping Review
Bibliographic record
Abstract
Developing partnerships among patients and healthcare providers improves quality of virtual care. Successful patient engagement is influenced by digital literacy. Although adults (35-64) with chronic health challenges may be motivated to use virtual services, they may not have the required skills or orientation to effectively participate on their virtual team. This scoping review aimed to identify resources available to enable adults with chronic health challenges to participate as partners on their virtual teams. Peer-reviewed and grey literature data from 2011 to 2022 were searched. A total of 432 peer-reviewed and 357 grey literature sources were retrieved and screened, and 14 and 84 sources, respectively, met the inclusion criteria. Relevant information from the sources was extracted and analyzed in duplicate and synthesized qualitatively. Key findings include (1) virtual workflow processes/frameworks, (2) 'webside manner' guidelines which emphasize "the how" as opposed to "the what" of facilitating team interactions, and (3) virtual patient support personnel. Overall, analyses suggest there are persisting gaps to be addressed in synchronous virtual care resources for adults with chronic health challenges.
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 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".