IVCO2 Training Effectiveness Study
Bibliographic record
Abstract
Etiometry’s Lead Clinical Specialist and a Human Factors Psychology Professor conducted a study to assess the effectiveness of training on how to use a new risk index, IVCO2, at The Hospital for Sick Children in Toronto, Ontario. Ten clinicians were each separately trained by the Lead Clinical Specialist and afterward the Human Factors Professor administered a ten question assessment. Immediately following the assessment, the professor interviewed each clinician, to obtain feedback about T3 which may be used to inform future enhancements user interface design changes, and/or training changes. Assessment results indicate that a majority of clinical users who are trained using existing materials will be able to interpret and use T3’s new IVCO2 Index safely and effectively. However, 30% of the study participants answered at least one assessment question wrong. This suggests that IVCO2 training should be enhanced. Meanwhile, interview data revealed that all study participants believe that Etiometry’s software provides users with relevant, clinically useful information and capabilities. However, the study’s results also suggest that users must climb a steep learning curve before they can use it effectively. It may helpful to train novice users in phases, so that they have multiple opportunities to learn about some of the features that they may not use immediately could find helpful as they start to use Etiometry’s software more.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".