Feedback on video skill: A concept analysis
Bibliographic record
Abstract
Increasing rates of mental health concerns are seen among youth in post-secondary institutions, particularly those enrolled in clinical-based health programs such as nursing. Nursing students are required to demonstrate skill competence for successful completion of nursing programs. Recent studies show that when students and faculty are engaged in video and audio recording of their own skills or co-creating video skills, many positive outcomes emerge, including a positive influence on their mental health. However, these videos skills are often overlooked by faculty. We explore the concept of "feedback on video skills" and its pedagogical and ethical implications for health and allied health practitioners within the context of flexible learning environments. Walker and Avant's (2011) concept analysis methodology was used. We identified the quantitative attributes and characteristics of "feedback on video skills" and presented sample cases to illustrate the concept further and guide the design and application of an online feedback video toolbox resource. Feedback is an important dimension of video skill teaching and learning. While faculty (expert) feedback on clinical skills is paramount in nursing education, other forms of feedback can be as valuable. This concept analysis method highlighted quantitative elements of feedback but left gaps in our understanding of the social relations and ethical considerations involved in using videorecorded feedback as a pedagogical tool. We suggest to further consider the use of video-recorded feedback through the lens of socio-technical affordances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".