Bibliographic record
Abstract
implemented, everyone has great expectations for immediate improvements in productivity.However, as the implementation begins the staff 's productivity goes down abruptly.4 Not only does productivity decline, but possible conflicts could arise.Various reasons exist for the temporary losses in productivity such as the time spent on training and self learning on the new system, adjusting to new procedures and working relationships, dealing with unrelated pre-existing problems surfaced by the change, calming the anxieties and fears of loss of security, autonomy, control, or respect and self esteem if the system is not quickly mastered.These issues might cause some people to stop using the new system and revert to the "good old way" of doing things.Assuming that adequate communication and training were completed earlier, you need to maintain your sense of perspective, be very visible to the staff, have good communication, and provide some end stage fun-possibly a celebration for the implementation process and where you are today.Since more than 50% of information systems either fail or people fail to use the system to its full capacity, the preparation, action, and maintenance stages need to be completed properly.If not, frustration may result and lead to a higher probability of failure.Unfortunately, we have no magic dust to make the transition to ehealth applications easy.But if the issues outlined here are ignored, you might end up continuously reinventing the wheel.
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 | 0.012 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".