Bibliographic record
Abstract
To date, efforts to initiate future graduates and nurses currently in practice into the world of information and communication technologies (ICTs) have been provided by relatively few nurse educators.In the past decade, nursing informatics leaders have developed a profile of informatics competencies for nurses, novice to expert -for example, the National Nursing Informatics Project (Hebert 2000) -and have demonstrated actual (Kaminski 2006) and possible informatics integration into curricula (Nagle 2001).Findings from recent studies (Nagle and Clarke 2004; Infoway 2007) suggest that a minority of Canadian schools of nursing have tackled the challenge of integrating informatics throughout their nursing curricula.When asked to respond to a recent survey (Infoway 2007), some schools decided not to participate because there were no faculty members with appropriate expertise in the area, while other schools did not regard informatics as relevant content for their program.Nevertheless, over the years, several schools of nursing have had the foresight to create a single informatics course -usually an electiveat the undergraduate or graduate level.I have held discussions with several deans and directors of schools of nursing over the past two years, and most of them recognize that this is a content area to be reckoned with sooner rather than later.No graduate nursing program has as yet created an informatics specialty option, but stay tuned.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".