Bibliographic record
Abstract
Consider the past decade of nursing research in Canada. A critical mass of nurses, at various stages of their research careers, has successfully competed for research personnel awards; nurses have established innovative programs of research; and new graduate programs for nurses have flourished. These are impressive achievements. Yet nursing research in Canada is at a critical juncture. As we look ahead to the next decade, we are challenged to build on the successes and momentum achieved, to conduct research that yields knowledge of relevance to a wide range of end users and to maintain the creative edge required for scholarly inquiry that is not exclusively driven by funding opportunities. These challenges raise many “how to” questions: how to provide strong mentorship for new researchers; how to optimize opportunities afforded through interdisciplinary teamwork; and how to reduce the lag time from research completion to uptake. In response to such questions, new funding opportunities have arisen. Among these are personnel awards for clinician scientists and nursing chairs and interdisciplinary and interinstitutional training centres (Edwards et al. 2002). These initiatives are creating a solid base of research infrastructure. With plans for building this infrastructure now in place, I think we should turn our attention to another challenge: ratcheting up our research efforts to make a difference at the systems level. I will describe why this is important and, from the point of view of a researcher whose primary base is in academia, suggest several ways this might be achieved.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.140 | 0.162 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.025 | 0.065 |
| Scholarly communication | 0.038 | 0.050 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".