Paramedicine informatics – leveraging advancing technology to drive positive change
Bibliographic record
Abstract
As all areas of healthcare continue to experience rapid digitisation, the field of health informatics is becoming increasingly important. Large health systems are employing more informatics professionals to ensure that the health information systems deployed are safe, efficient, equitable and, most importantly, adopted by users. The value of informatics to paramedicine is only increasing as we continue to see rapid expansion of digital health technologies. New wearable technologies, improved cellular infrastructure, and leaps in generative artificial intelligence and natural language processing capabilities have made informatics even more relevant to paramedicine. However, we need paramedicine professionals with the necessary informatics competencies in place to take advantage of the opportunities provided by informatics. Health informatics can improve our systems and quality of care by using maturity models, artificial intelligence evaluation frameworks, implementation science, and health data interoperability. Given the unique context of paramedicine in healthcare, we recommend that a specialised informatics subdivision of paramedicine informatics be recognised as a pathway to further professional growth and distinguish paramedicine as a unique healthcare profession with specific knowledge frameworks and needs.
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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".