Feasibility and Effectiveness of Self-Management Education and Coaching on Patient Activation for Managing Cancer Treatment Toxicities
Bibliographic record
Abstract
BACKGROUND: Poorly managed cancer treatment toxicities negatively impact quality of life, but little research has examined patient activation in self-management (SM) early in cancer treatment. METHODS: We undertook a pilot randomized trial to evaluate the feasibility, acceptability, and preliminary effectiveness of the SMARTCare (Self-Management and Activation to Reduce Treatment Toxicities) intervention. This intervention included an online SM education program (I-Can Manage) plus 5 sessions of telephone cancer coaching in patients initiating systemic therapy for lymphoma or colorectal or lung cancer at 3 centers in Ontario, Canada, relative to a usual care control group. Patient-reported outcomes included patient activation (Patient Activation Measure [PAM]), symptom or emotional distress, self-efficacy, and quality of life. Descriptive statistics and Wilcoxon rank-sum tests were used to examine changes over time (baseline and at 2, 4, and 6 months) within and between groups. We used general estimating equations to compare outcomes between groups over time. The intervention group completed an acceptability survey and qualitative interviews. RESULTS: Of 90 patients approached, 62 (68.9%) were enrolled. Mean age of the sample was 60.5 years. Most patients were married (77.1%), were university educated (71%), had colorectal cancer (41.9%) or lymphoma (42.0%), and had stage III or IV disease (75.8%). Attrition was higher in the intervention group than among control subjects (36.7% vs 25%, respectively). Adherence to I-Can Manage was low; 30% of intervention patients completed all 5 coaching calls, but 87% completed ≥1. Both the continuous PAM total score (P<.001) and categorical PAM levels (3/4 vs 1/2) (P=.002) were significantly improved in the intervention group. CONCLUSIONS: SM education and coaching early during cancer treatment may improve patient activation, but a larger trial is needed. CLINICALTRIALS: gov Identifier: NCT03849950.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".