Educational Technology Use in Neurodiagnostic Clinical Skills Training
Bibliographic record
Abstract
The current shortage of clinical sites for neurodiagnostic technology (NDT) students is limiting enrollments and subsequently limiting graduates from NDT schools in the U.S. A lack of knowledge or consensus concerning the use of educational technology in NDT clinical skills training prompted this investigation. The purpose of this study was to explore the use of educational technology in providing NDT clinical skill training. This qualitative Delphi study was guided by experiential learning theory and cognitive constructionist epistemology. Thirty expert panelists were recruited to rate the effectiveness of educational technology methods in addressing neurodiagnostic competencies for electroencephalography. Twenty-four completed round one, twenty-two completed round two and nineteen completed the third and final round. The competencies were derived by combining national competencies or practice analysis from the United States, Australia, Canada and the United Kingdom for neurodiagnostic technologists performing electroencephalography (EEG). Results of the three rounds of the Delphi study were processed using the mean value and interquartile deviation for evaluation of consensus. Consensus among the expert panelists supported the potential effectiveness of educational technology to address neurodiagnostic graduate competencies for technologists performing EEG. In conclusion, the expert panel consensus was NDT clinical skills for performing EEG can be addressed using educational technology, followed by a post-graduate clinical residency. Using educational technology and a post-graduate residency could increase school capacity. An increase in graduate numbers would help sustain the existing schools, better supply the profession, and increase public access to quality neurodiagnostic care.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".