Bibliographic record
Abstract
A key strategy for advancing a Magnet® recognized (American Nurses Credentialing Center, Sliver Spring, MD) culture of excellence is ongoing staff development. The Magnet Recognition Program® requires that there should be evidence in recognized organizations of the development, dissemination and enculturation of the 14 Forces of Magnetism. Magnet Force 14, Professional Development sets an expectation that organization’s value personal and professional growth, including orientation, career development, formal education and continuing education. Magnet Force 11, Nurses as Teachers expects that nurses be involved in educational activities. Implementation of Magnet Force 8, Consultation and Resources requires that adequate human resources and knowledgeable experts be available to consult and serve as mentors. Houston Methodist Hospital (HMH) has been designated as a Magnet facility since 2002. As the hospital prepared for its fourth Magnet re-designation, a knowledge deficit and learning need was identified resulting not only from the influx of new employees, many of whom had not worked in a Magnet designated organization, but also from the routine preparation that occurs during re-designation. In addition to these learning needs, there was a concern that adding a significant number of new employees could potentially influence the organization’s culture. This article will address the resources and strategies used to engage adult learners in becoming knowledgeable and vested in the Magnet program and their role and responsibilities in this environment to advance a culture of excellence, as defined by the full expression of the 14 Forces of Magnetism.
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".