Videoconferencing a stroke assessment training workshop: Effectiveness, acceptability, and cost
Bibliographic record
Abstract
INTRODUCTION: Videoconferencing (VC) is becoming a common method for the delivery of continuing education (CE) to clinicians in remote locations. The purpose of this study was to compare the effectiveness, acceptability, and costs of a full-day training workshop (TW) delivered through two different formats: face-to-face (FTF) and VC. The TW was designed to teach administration and scoring guidelines for the Chedoke-McMaster Stroke Assessment, an outcome measure used by rehabilitation professionals. METHODS: The TW was delivered simultaneously in FTF and VC formats to a total of five remote communities on two separate occasions. Participants completed a test of scoring competency at the beginning (pretest) and end (posttest) of the TW as well as a feedback questionnaire. A cost comparison was also undertaken. RESULTS: Forty-four physical and occupational therapists participated. No significant between-group differences were found in posttest scoring competency related to delivery format (FTF or VC): (F(1,38) = 0.6, MSE = 3.6, p > 0.4), or for the two workshops: (F(1,38) = 1.4, MSE = 3.6, p > 0.2). Despite technical difficulties, participant experience was rated as "good" to "excellent." The VC method offered considerable cost savings to participants and their organizations, at a minimum of $7,437 (Canadian). CONCLUSION: Clinicians participating in the TW via VC performed as well as those in the FTF group on the competency test. Videoconferencing improves access to CE, is well received by participants, and provides a cost-effective method of course delivery. Further evaluation of other CE events delivered through VC is indicated.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".