Impact of a Training Program on Oncology Nurses’ Confidence in the Provision of Self-Management Support and 5As Behavioral Counseling Skills
Bibliographic record
Abstract
Background: Cancer patients and their families play a central role in the self-management of the medical, emotional, and lifestyle consequences of cancer. Nurses with training in self-management support can enable cancer patients to better manage the effects of cancer and treatment. Methods: As part of a randomized controlled trial, we developed a training program to build nurses’ confidence in the provision of self-management support (SMS). The SMS skills taught were adapted from the Stanford Peer Support training programs and embedded within the 5As (Assess, Advise, Agree, Assist, and Arrange) behavioral counseling process. We evaluated the impact of the training program on oncology nurses’ and coaches’ confidence using a Student’s t-test for paired samples in a nonrandomized, one-group pre/postsurvey. Results: Participants were experienced oncology nurses from three participating cancer centers. A two-tailed Student’s t-test for paired samples showed a significant improvement in nurses’ confidence for the 15 SMS microskills targeted in the training between the pretest and post-test as follows: for Center 1, a mean difference of 0.79 (t = 7.18, p ≤ 0.00001); for Center 2, a mean difference of 0.73 (t = 8.4, p ≤ 0.00001); for Center 3, a mean difference of 1.57 (t = 11.45, p ≤ 0.00001); and for coaches, a mean difference of 0.52 (t = 7.6, p ≤ 0.00001). Conclusions: Our training program improved oncology staff nurses’ and cancer coaches’ confidence in 15 SMS microskills and has potential for SMS training of nurses in routine care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".