Bibliographic record
Abstract
The world is changing. You are probably practising next to a computer, and then it changes. Yes, an incremental change within a version, is no problem. However, if you change groups, or even within a group, external or internal forces will lead to changes, in which programmes you are using and how. The new rural medical resident becomes an expert at changing systems as each rotation may start with the challenge of learning how to use new informatics. Such a recent change at our rural hospital has led me to think how fortunate we can be in rural settings when dealing with change management. It has never been pleasant to change, but a supportive environment is essential. One of the advantages of rural is that dealing with occasional stuff that we are not experts in is a strength of the rural generalist physician. Frankly, it is easier to do these transitions rurally where people, as an abstract concept, do not really exist and instead, you have interaction with a collection of persons. You must have some buy-in. Even if it is a decision imposed from the city, starting the day with wanting to make the best of it is a place you want to be. Then you need the support. You need physical support (many more computers and paraphernalia) and they need to be there before you need them. This is hard to do at a hospital. Space needs to be allocated and walls moved, desks installed with phone and Internet connections. Management needs to realise that the transition will fail if this does not happen on time. Let us not forget that you need training on the new software. I grant that most training ahead of time will be insufficient as you will be looking at an interface without the context of real-life tasks and workflow. This brings up the training at the school of hard knocks. What worked with us is a small cadre of volunteer helpers. You can call them ‘super users’, but the concept is understood. They are nothing more special than people whose presence is entirely to support you. These are your peers who may have received a few hours more training in the test environment and help you muddle through right then and there. Needless to say, you also need a real ‘super user’ consultant or two to pull in when two heads and some clicking are not getting anywhere. That is it for a successful transition. Pity on those who are in the city.
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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".