A Scoping Review of Virtual Focus Group Methods Used in Rehabilitation Sciences
Bibliographic record
Abstract
Virtual methods for conducting focus group studies are increasingly being used in many fields, including rehabilitation sciences. This is partly due to the current pandemic, and the need for social distancing, however, may also relate to factors such as convenience and practicality. Virtual research methods enable investigators to collect data at a distance from the participant(s) through the use of technology-mediated data collection methods incorporating new tools and technologies. The aim of this scoping review was to identify, synthesize, and present current evidence related to the methods for conducting virtual focus groups. A comparison of asynchronous and synchronous data collection methods was conducted. The objectives, inclusion criteria, and scoping review methods were specified in advance and documented in a protocol. The 40 articles in this review included virtual focus group research conducted in rehabilitation sciences including data collection conducted using both synchronous (22.5%) and asynchronous (77.5%) models and using a defined moderation method. Three modes of focus group discussion were reported including email, chat-based, and videoconferencing; these were facilitated through the various technology platforms reported in the review. Reported barriers and facilitators to conducting virtual focus group research were extracted and summarized. Commonly reported facilitators to virtual focus group research included the ability to recruit participants from diverse geographical locations and the participants’ ability to engage at times convenient to them. Both computer literacy and access to technology were reported as common barriers. This review highlighted the need for further research and guidance around virtual focus groups conducted using face-to-face synchronous methods and with younger participants groups.
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 | 0.038 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".