Decisión acertada: desbloquear el potencial de los macrodatos para los reguladores de enfermería
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aim This paper explores the potential for incorporating big data in nursing regulators' decision-making and policy development. Big data, commonly described as the extensive volume of information that individuals and agencies generate daily, is a concept familiar to the business community but is only beginning to be explored by the public sector. Background Using insights gained from a recent research project, the College and Association of Registered Nurses of Alberta, in Canada is creating an organizational culture of data-driven decision-making throughout its regulatory and professional functions. The goal is to enable the organization to respond quickly and profoundly to nursing issues in a rapidly changing healthcare environment. Sources of evidence The evidence includes a review of the Learning from Experience: Improving the Process of Internationally Educated Nurses' Applications for Registration (LFE) research project (2011–2016), combined with a literature review on data-driven decision-making within nursing and healthcare settings, and the incorporation of big data in the private and public sectors, primarily in North America. Discussion This paper discusses experience and, more broadly, how data can enhance the rigour and integrity of nursing and health policy. Conclusion Nursing regulatory bodies have access to extensive data, and the opportunity to use these data to inform decision-making and policy development by investing in how it is captured, analysed and incorporated into decision-making processes. Implications for Nursing and Health Policy Understanding and using big data is a critical part of developing relevant, sound and credible policy. Rigorous collection and analysis of big data supports the integrity of the evidence used by nurse regulators in developing nursing and health policy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it