Virtually present: The perceived impact of remote facilitation on small group learning
Bibliographic record
Abstract
BACKGROUND: The engagement of facilitators located remotely for small group learning has received little research attention. However, this approach could increase the pool of experts for small group learning, thus addressing challenges to sustainability faced by in-person models of small group facilitation. AIM: The objective of this study was to describe the experiences and perceptions of students regarding the use of remote facilitation for small group learning in a health education setting. METHODS: This qualitative study involved three focus groups (n = 16) composed of students in the advanced neuromusculoskeletal teaching unit in the University of Toronto, Department of Physical Therapy. Focus groups were audio-taped and transcribed verbatim, and data were analyzed thematically. RESULTS: Three main influences emerged related to the experiences of students regarding the use of remote facilitation for small group learning in a health education setting: technology (including audio and visual), facilitator (including quality of facilitation and facilitator expertise), and group dynamics (including ground rules, roles and responsibilities, and learning style). Each of these influences acted independently and interdependently to shape participants' perceptions. CONCLUSION: This study prompts a widening of the concept of distance learning to also include distance teaching, which may have wide applicability to health profession programs.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".