Bibliographic record
Abstract
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Having enough staff to provide high-quality care to cancer patients will become a growing issue across Canada over the next decades. Statistical predictions indicate that both the number of new diagnoses and the prevalence of cancer will increase dramatically in the next two decades. When combining these trends with the simultaneous trend toward health human resource shortage in Canada, the urgency of assuring we have adequate staff to deliver cancer care becomes clear. This research study focuses directly on oncology nurses. Guided by the grounded theory methodology, this research study aims to formulate a strategic, proactive peer preceptorship program through a four-phased research process. The goal of this research is to develop a program that will support experienced staff members to fully implement their role as a preceptor to new staff, to facilitate effective knowledge transfer between experienced staff to the new staff members, and to assure new staff members are carefully transitioned and integrated into the complex ambulatory cancer care workplaces. In this article, the data from the first phase of the research project will be explored specifically as it relates to establishing the foundation for the development of a provincial ambulatory oncology
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.401 | 0.154 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".