Pain Self-Management Behaviors in Breast Cancer Survivors Six Months Post-Primary Treatment: A Mixed-Methods, Descriptive Study
Bibliographic record
Abstract
Background/Objectives: One-third of breast cancer (BC) survivors experience chronic treatment-related pain (CTP) that requires multimodal management strategies, which may include pain self-management behaviors (PSMBs). Most studies exploring PSMBs focus on patients with advanced cancer, who may differ from survivors in their pain management needs and access to resources. This mixed-methods study explored PSMBs of survivors of BC, referral sources, and goals for pain relief, and examined the relationship between PSMB engagement and pain intensity/interference. Methods: Survivors of BC who were six months post-treatment completed measures assessing their pain intensity/interference and PSMB engagement. Purposive sampling identified a subset of participants who completed interviews, which were analyzed using thematic analysis. Results: Participants (n = 60) were 60 ± 10 years old. Worst Pain Intensity and Pain Interference were 3.93 ± 2.36 and 2.09 ± 2.11, respectively. Participants engaged in 7 ± 3.5 PSMBs. The most common were walking (76%) and distraction (76%). PSMBs described in the interviews (n = 10) were arm stretching and strengthening exercises, seeking specialized pain management services, and avoidance. Most PSMBs were self-directed or suggested by friends. All pain relief goals were to minimize pain interference. PSMB engagement was not associated with Worst, Least, or Average Pain Intensity (all rs ≤ −0.2, p ≥ 0.05) but was associated with Pain Interference (rs = 0.3, p ≤ 0.01). Conclusions: The survivors of BC engaged in many PSMBs, with varying levels of effectiveness and a varying quality of supporting evidence. Most PSMBs were self-directed and some required intervention from healthcare providers or other people, while others required access to limited specialized pain management services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".