Cultural adaptation of the Pan-Canadian Oncology Symptom triage and remote support practice guide for cancer-related fatigue in China: Integration of traditional Chinese medicine nursing evidence
Bibliographic record
Abstract
Objective: This study aimed at culturally adapting pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) Cancer-related fatigue (CRF) Practice Guide to enable its use in China. This article focuses on presenting the key cultural adaptation step: supplementing traditional Chinese medicine (TCM) nursing recommendations for CRF symptom management according to evidence. Methods: Guided by A Guideline Adaptation and Implementation Planning Resource (CAN-IMPLEMENT), the process for cultural adaptation of the CRF guide in the COSTaRS project included translation, expert committee review, acceptability and feasibility assessment, and targeted adaptation to include TCM nursing techniques for CRF management via the Delphi method. Results: First, an expert committee of nurses, nurse leaders, and researchers was established. The practice guide was translated and verified by the members of the expert committee. Nurses then rated the practice guide for acceptability and feasibility. Concurrently, 83 stakeholders (nurses and patients) identified five relevant TCM nursing techniques: acupuncture, moxibustion, acupressure therapy, Taijiquan, and auricular acupoint embedding. A systematic review of literature identified three clinical practice guidelines and four systematic reviews. Through two rounds of Delphi expert consultation, five TCM care strategies were added into the culturally adapted COSTaRS practice guide. Conclusions: Cultural adaptation of the Canadian CRF practice guide involved not only language translation but also the addition of relevant TCM evidence. Combining TCM evidence and the Delphi method was a novel aspect of the cultural adaptation process. Further research is needed to investigate the implementation of the guide in appropriate settings in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".