Engaging breast cancer survivors in their survivorship care.
Bibliographic record
Abstract
22 Background: In North America, increasing evidence supports including patients’ in managing their own health as a strategy to improve health-related outcomes. Moreover, publications have emphasized the merit of supportive interventions during the survivorship phase, given their association with improvements in overall quality of life. Breast cancer survivors (BCS) are integral stakeholders of their own health, and ensuring their educational needs are met may positively influence them to become actively involved in their survivorship care. This study’s aimed to evaluate BCS activation levels in their care following an end of treatment educational group intervention (EOT) on what to expect after treatment, including self-management tips. Increased activation levels are consistently associated with improved self-management behaviors and utilization of services. Methods: The Patient Activation Measure (PAM), which evaluates one’s knowledge, skills, beliefs, and confidence for managing health and health care, was voluntarily self-administered to BCS shortly before and immediately following the EOT, and interviewer administered by telephone at 1 and 6 months post EOT, respectively. Data were analyzed with parametric (paired t-tests) and non-parametric (Wilcoxon Signed rank tests) comparisons as appropriate to assess EOT impact on BCS’ activation levels. Floor and ceiling effects were considered present if more than 40% of BCS achieved the highest score possible. Results: The pre and post survey response rate was 92.8%, the 1 month and 6 months delayed post survey response rate were 72.4% and 46.4%, respectively (n = 69). Post EOT, BCS significantly increased their activation levels: t(64) = 4.291, p ≤ 0.001, t(50) = 5.982, p ≤ 0.001 to t(32) = 4.664, p ≤ 0.001 for the post, 1 month and 6-month post surveys, respectively. The effect size of the BCS’ increased activation level in their own health increased incrementally with time from small (d = 0.40), medium (d = 0.65) to large (d = 0.84), for the post, 1 month and 6 month surveys, respectively. Conclusions: Our results indicate a large effect size in BCS activation levels at 6 month post EOT, suggesting a significant increase in BCS’s engagement to take part in their survivorship care and overall health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".