A Reflection on the Mentorship Journey
Bibliographic record
Abstract
In 2023, for the first time, CANO-ACIO awarded three nursing students the Nursing Research Student Award for their successful abstracts. The award gave them an opportunity to present their research work during the annual CANO/ACIO conference that took place in Niagara Falls, Ontario. This innovative award approach was of particular importance for these three nursing students, one of whom was in an undergraduate nursing program, one in a Master’s program, and one in a doctoral program. As is evident in the descriptions below, the award was important to them because it gave them an opportunity to share their work with a national audience, as well as begin to see the larger world of oncology nursing research and its potential to change practice. Each of the individual presentations was amazing and greatly appreciated by all attendees of the Board-sponsored research workshop. Based on the feedback and the desire to profile the opportunity for other students in the future, each nursing student was invited to contribute to this Research Reflections Column. They were asked to discuss their personal experience and its impact on them. The following paragraphs are a summary of the synopsis they provided about their experiences.
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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.011 | 0.040 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".