Translating Guidelines to Practice: A Training Session about Cancer-Related Fatigue
Bibliographic record
Abstract
Background: Cancer-related fatigue (crf) is the highest unmet need in cancer survivors. The Canadian Association of Psychosocial Oncology (capo) has developed guidelines for screening, assessment, and intervention in crf; however, those guidelines are not consistently applied in practice because of patient, health care provider (hcp), and systemic barriers. Notably, previous studies have identified a lack of knowledge of crf guidelines as an impediment to implementation. Methods: In this pilot study, we tested the preliminary outcomes, acceptability, and feasibility of a training session and a knowledge translation (kt) tool designed to increase knowledge of the capo crf guidelines among hcps and community support providers (csps). A one-time in-person training session was offered to a diverse sample of hcps and csps (n = 18). Outcomes (that is, knowledge of the capo crf guidelines, and intentions and self-efficacy to apply guidelines in practice) were assessed before and after training. Acceptability and feasibility were also assessed after training to guide future testing and implementation of the training. Results: After training, participants reported increased knowledge of the capo crf guidelines and greater self-efficacy and intent to apply guidelines in practice. Participant satisfaction with the training session and the kt tool was high, and recruitment time, participation, and retention rates indicated that the training was acceptable and feasible. Conclusions: The provided training is both acceptable to hcps and csps and feasible. It could increase knowledge of the capo crf guidelines and participant intentions and self-efficacy to implement evidence-based recommendations. Future studies should investigate actual changes in practice and how to optimize follow-up assessments. To promote practice uptake, kt strategies should be paired with guideline development.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".