Educational Technology Use in Neurodiagnostic Clinical Skills Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it