Feasibility of Systems Support Mapping to guide patient-driven health self-management in colorectal cancer survivors
Bibliographic record
Abstract
Objective To evaluate feasibility of System Support Mapping (MAP), a systems thinking activity that involves creating a diagram of existing self-management activities (e.g. symptom management, health behaviors) to facilitate autonomous engagement in optimal self-management.Design One-arm pilot study of MAP in colorectal cancer survivors (NCT03520283).Main outcome measures Feasibility of recruitment and retention (primary outcome), acceptability, and outcome variability over time.Results We enrolled 24 of 66 cancer survivors approached (36%) and 20 completed follow-up (83%). Key reasons for declining participation included: not interested (n = 18), did not perceive a need (n = 9), and emotional distress/overwhelmed (n = 7). Most participants reported that MAP was acceptable (e.g. 80% liked MAP quite a bit/very much). Exploratory analyses revealed a −4.68 point reduction in fatigue from before to 2 weeks after MAP exceeding a minimally important difference (d = −0.68). There were also improvements in patient autonomy (d = 0.63), self-efficacy (for managing symptoms: d = 0.56, for managing chronic disease: d = 0.44), psychological stress (d = −0.45), anxiety (d = −0.34), sleep disturbance (d = −0.29) and pain (d = −0.32). Qualitative feedback enhanced interpretation of results.Conclusions MAP feasibility in colorectal cancer survivors was mixed, predominantly because many patients did not perceive a need for this approach. MAP was acceptable among participants and showed promise for improving health outcomes.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".