Making the Most of Medical Orientation — A New Approach
Bibliographic record
Abstract
Orientating new junior medical staff can be a complex and time consuming task. Traditional models have typically involved a day or longer of lectures. This involves a large number of senior staff being available on the first day of term. It also means that junior staff not present on the first day had any access to an orientation program at all. Evaluation of our program confirmed the belief that the day was dull and that there was simply too much information for new staff to absorb. As a result of this feedback we extensively updated our orientation program. Pre-reading of the junior staff manual became compulsory. We departed from the traditional lecture style program and devised a new ten- station scenario based interactive program. The stations were designed to cover aspects of the hospital's mandatory education and key educational requirements in order to function effectively on our campus. Station leaders were selected and trained in the goals of the new process. Several of our secondment sites were engaged in the development of the project topics. We hoped that our secondment sites would be relieved of some orientation responsibility if core material was delivered centrally. The strength of the new orientation is that it is portable, reproducible and uniform. It is also available via video conferencing. A single person can educate new staff in three hours if the need arises. Most importantly all new staff will have access to the program within a week of starting a term at our hospital. Key words: medical orientation; junior staff; interactive.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".