Computers and Nursing – What is happening?
Bibliographic record
Abstract
“Electronic health records are one of the keys to modernizing the health system and improving access and outcomes for Canadians.” – Commission on the Future of Health Care in Canada, led by Roy Romanow Did you know that Health Canada has invested over a billion dollars in accelerating the electronic health record systems? Did you know that there are multiple national and provincial electronic health record projects in the works right now funded by this investment? Did you know that there are many hundreds of individuals involved in these projects? Did you know that there are hardly any RNs working on these projects? I am one of those few... So let me catch you up on what is happening... Canada Health Infoway, Inc (Infoway) (www.infoway-inforoute.ca) was created in 2002 as an accelerator for the development of electronic health records in Canada. Hundreds of millions of dollars have been invested in various projects throughout the country. The projects are funded through the provincial health organizations, and in some cases, through regional or organizational consortiums, to support the acceleration of the fundamental systems required for Infoway’s vision:
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".