Bibliographic record
Abstract
BACKGROUND: The advent of a more planned approach to workforces has led to increased interest in knowing exactly what workforces do, how much they cost and who their members are. Heightened interest in health human resources has highlighted the need for accurate definitions of nurse and nursing role and emphasized the importance of accurate, valid and timely workforce data. AIM: This commentary addresses some of the statements made in this issue by Currie and Carr-Hill (pp. 67-74) who ask, 'What is a nurse? Is there an international consensus?' General remarks about the importance of nursing health services data are also provided. DISCUSSION: It is not surprising that nursing is struggling with standardization of definitions and role descriptions within and across countries. Globalization has intensified the need to understand complex relationships between systems such as education, finance and health that often differ from country to country. CONCLUSION: Professional and/or regulatory nursing associations can facilitate standardization of definitions and strengthen data collection; both of which are imperative. The more titles we create, the more difficult it becomes to have a generalized taxonomy of nursing services locally, nationally and internationally. Working closely with agencies like the International Council of Nurses and the World Health Organization may lead to a much-needed consensus on common indicators within and across countries. Given the large size of the nursing workforce worldwide, nurses can play an important role in the creation of comparable data that can be used to set policies that will affect the delivery of quality health care.
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 | 0.320 | 0.635 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.022 | 0.063 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.018 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".