Health policy and nursing : influence, development and impact
Bibliographic record
Abstract
Emerging Themes D.Hennessy Influencing Policy and the Impact of Health Policy on Nursing Policy in the United States C.Jennings Nurses' Influence on Health Policy in Australia S.Legg & S.Zyntek Is there an International Nursing Policy? T.Ride The Impact of Health Policy on Nursing Policy and Vice Versa C.Birt The Impact of Health Care Reforms on Nursing: a European Perspective A.Fawcett-Hennesy The European Oncology Society N.Jodrell & K.Redmond Nurses Influence Policy Change in Education in Canada N.Murphy A European Perspective of the Impact of Policy on Nursing Education T.Keighley The Nursing Contribution to Health Services Research and Development E.Scott Research-Practice Interface: The Barriers of Policy and Personality C.Hicks Implications of Policy Development for the Nursing Profession P.Spurgeon From Profession to Commodity: The Case of Community Nurses P.Gough & N.Walsh Influencing and Implementing Reforms S.Fry A Final Postscript D.Hennessy & P.Spurgeon
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".