Using Kane’s framework to build an assessment tool for undergraduate medical student’s clinical competency with point of care ultrasound
Bibliographic record
Abstract
INTRODUCTION: Point-of-care ultrasonography (POCUS) is a portable imaging technology used in clinical settings. There is a need for valid tools to assess clinical competency in POCUS in medical students. The primary aim of this study was to use Kane's framework to evaluate an interpretation-use argument (IUA) for an undergraduate POCUS assessment tool. METHODS: Participants from Memorial University of Newfoundland, the University of Calgary, and the University of Ottawa were recruited between 2014 and 2018. A total of 86 participants and seven expert raters were recruited. The participants performed abdominal, sub-xiphoid cardiac, and aorta POCUS scans on a volunteer patient after watching an instruction video. The participant-generated POCUS images were assessed by the raters using a checklist and a global rating scale. Kane's framework was used to determine validity evidence for the scoring inference. Fleiss' kappa was used to measure agreement between seven raters on five questions that reflected clinical competence. The descriptive comments collected from the raters were systematically coded and analyzed. RESULTS: The overall agreement between the seven raters on five questions on clinical competency ranged from fair to moderate (κ = 0.32 to 0.55). The themes from the qualitative data were poor image generation and interpretation (22%), items not applicable (20%), poor audio and video quality (20%), poor probe handling (10%), and participant did not verbalize findings (14%). CONCLUSION: The POCUS assessment tool requires further modification and testing prior before it can be used for reliable undergraduate POCUS assessment.
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 | 0.125 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".