Enhancing Nurses’ Oral Therapy Practice in 4 Latin American Countries
Bibliographic record
Abstract
BACKGROUND: Oral therapy (OT) use for cancer is increasing globally. Yet, nurses in 4 Latin American countries lacked knowledge and educational opportunities to safely care for people receiving OTs. Global partnerships to contextualize education and create local capacity may enhance nursing practice. OBJECTIVE: Within 4 Latin American countries, this study aims to (1) develop, deliver, and evaluate an OT cancer nursing education program and (2) evaluate the feasibility and efficacy of using an integrated knowledge translation (iKT) framework to develop the program and foster nurses' capacity for OT care. METHODS: Using the iKT framework, a "train the trainer" model was used to develop, contextualize, pilot test, implement, and evaluate the OT education program. An online survey evaluated nurses' perceived benefits, ease of use, barriers, facilitators, and recommendations for improvement. Nurses' self-reported OT practices were evaluated 9 months after the final workshop. RESULTS: One hundred nineteen nurses across 4 countries participated in a pilot and/or final OT educational workshop, facilitated by 6 local nurse champions. The nurse champions found the program easy to use and modify. Participants reported using the curriculum to teach other nurses and patients and networking opportunities for problem solving. Barriers included nurses' role clarity and time for education. CONCLUSIONS: The iKT approach was an effective method to develop the OT curriculum and build OT capacity among nurses and leaders within the 4 countries. IMPLICATIONS FOR PRACTICE: The iKT approach may be useful in low- or middle-income countries to enhance nursing education and practice. Future OT education projects should strengthen strategies for ongoing support after education intervention.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".