Who is Drinking Our Kool-Aid? Hear Clinicians’ Perspective of their Human Factors Practitioner
Bibliographic record
Abstract
Human factors (HF) has multiple domains that integrate various disciplines; and its principles and methods can be diversely applied within an organization. Healthcare organizations have started to deploy HF Practitioner (HFP) to assist in enhancing patient safety. However, the path for HFP integration into a hospital is still immature, clinical staff may be unclear of how to effectively collaborate with their HFP, and what benefits could HFP provide. This panel brings in 5 panelists from different organizations, who will share their experience in collaborating with their clinical advocate. Most importantly, audiences will hear their clinician advocates’ perceptions of the collaborations and benefits that their HFP has delivered, which encouraged them to drink our HFP ‘Kool-Aid.’
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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.026 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.024 | 0.041 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".