Evaluation of an educational program for nurses providing cancer symptom management: The pan-Canadian Oncology Symptom Triage and Remote Support Online Tutorial
Bibliographic record
Abstract
Purpose: To evaluate the acceptability of the pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) open-access online tutorial and its impact on nurses' knowledge and perceived confidence in symptom management. Methods: Retrospective pre-/post-test evaluation of nurses who completed the tutorial knowledge test and/or acceptability survey. The tutorial was modeled after the previously evaluated in-person workshop to prepare nurses providing cancer symptom management using COSTaRS practice guides. Results: From 2017-2021, 743 nurses completed the knowledge test, and 749 nurses evaluated the tutorial. Mean knowledge score was 4.4/6 and 83% of participants achieved passing scores. Compared to pre-tutorial, nurses improved their perceived confidence in assessing, triaging, guiding patients in self-care (p<0.001), and ability to use the COSTaRS guides (p<0.001). Nurses rated the tutorial as easy to understand (95%), just the right amount of information (92%), providing new information (75%), overall good to excellent (89%), and would recommend it to others (83%). Conclusions: More than 700 nurses accessed the tutorial. After completion, nurses demonstrated good knowledge and improved perceived confidence in cancer symptom management.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".