Discourse / Discours : Nursing's Valued Resources: Critical Issues in Economies and Nursing Care
Bibliographic record
Abstract
Nursing's first steps into the 21st century have been charted by a set of pressing economic issues around the composition and allocation of nursing resources. Buffeted by a deepening nursing shortage of global dimensions, health-care demand that is outpacing resources, and consumer demands for safe, effective, and responsive care, nursing finds itself at a critical juncture regarding two fundamental questions: what resources (financial and other) are necessary to build and maintain a qualified and effective nursing workforce, and how can nursing resources be most effectively allocated to meet evolving health-care needs. The current plight provides us with an opportunity to address such questions in new and creative ways, by critically examining how we are supporting the current nursing workforce and preparing the future one, the settings and roles in which the workforce is deployed, and the degree to which decisions in these matters are based on research that demonstrates the most cost-effective ways of allocating nursing care. The aggressive pursuit of these questions requires the input of every branch of the profession: practice, administration, education, research, and policy. It also requires that we grapple with the following issues. First, nursing finds itself once again battling a workforce shortage. Though the causes may vary, this shortage has serious implications for the kind of nursing care we are able to provide and for how well current and future health-care needs will be met. While we have stared down previous nursing shortages, largely through short-term financial
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".