Bibliographic record
Abstract
As we begin the year 2024, we do so with some very big challenges that have spilled over from 2023 and, indeed, many years before that.Every day, we are confronted with concerning experiential and research-based evidence about worsening access to healthcare, pervasive racism and widening disparities.Clearly, there is a great deal of work to be done in our healthcare system to support and improve the health of the diverse populations that we serve.Yet, along with the challenges come opportunities to reflect, collaborate, innovate, evaluate and learn.When I look at issues of the Canadian Journal of Nursing Leadership (CJNL) from the past 20 years, I am astounded at how some concerns have changed and some have remained the same.Can you believe that there was a time when nursing positions in practice and in education were actually being cut?Of course, many of the big issues we face today were emerging even then, and we have long since passed the tipping point that has put the country into a healthcare crisis.Crisis is a fertile ground for innovation, and the windows of opportunity for nursing to contribute to addressing these big challenges are wide open.Nursing leadership matters, and the need for all nurses to lean into leadership roles has never been more imperative.At the CJNL, we have exciting plans to help contribute to the leadership tsunami that is vital to the present and the future, and I would like to share some of those plans with you.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.053 | 0.020 |
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".