Self-Management Support for Cancer Survivors: A Descriptive Evaluation of the Symptom Navi Training from the Perspective of Health Care Professionals
Bibliographic record
Abstract
The Symptom Navi Program (SNP) is a self-management support (SMS) intervention for people with cancer. It consists of self-management supportive leaflets, educational conversations, and two standardized training sessions. A descriptive quality evaluation method was used to evaluate SNP implementation across 14 cancer services from 2021 to 2024. We evaluated training content, methods, and participants' confidence to use SMS in their clinical routine. Nurses, social workers, and psychologists completed ad hoc closed and open-ended questions after each training. The Work Sense of Coherence (Work-SoC) scale was used to elicit participants' self-reported perceptions of their work context at cancer services. A series of descriptive analyses were conducted on the Work-SoC scale, the training content, and the methods. In addition, training-specific questions and predefined hypotheses were correlated. Thematic analysis was employed to examine open-ended questions. The SNP training content and methods largely met participants' needs. Participants' confidence in applying educational conversations decreased over time. The findings suggest a robust correlation between the application of educational conversations in daily routines and the participants' perceptions regarding the comprehensibility and manageability of their work situations. Future research focusing on the implementation of SMS in clinical practice should examine the work context.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".