Alberta CancerBridges Development of a Care Plan Evaluation Measure
Bibliographic record
Abstract
Background: No standardized measures specifically assess cancer survivors’ and healthcare providers’ experience of Survivor Care Plans (SCPS). We sought to develop two care plan evaluation (CPE) measures, one for survivors (CPE-S) and one for healthcare providers (CPE-P), examine initial psychometric qualities in Alberta, and assess generalizability in Manitoba, Canada. Methods: We developed the initial measures using convenience samples of breast (n = 35) and head and neck (n = 18) survivors who received scps at the end of active cancer-centre treatment. After assessing Alberta’s scp concordance with Institute of Medicine (IOM) recommendations using a published coding scheme, we examined psychometric qualities for the CPE-S and CPE-P. We examined generalizability in Manitoba, Canada, with colorectal survivors discharged to primary care providers for follow-up (n = 75). Results: We demonstrated acceptable internal consistency for the cpe-s and cpe-p subscales and total score after eliminating one item per subscale for cpe-s, two for cpe-p, resulting in revised scales with four 7-item and 6-item subscales, respectively. Subscale scores correlated highly indicating that for each measure the total score may be the most reliable and valid. We provide initial cpe-s discriminant, convergent, and predictive validity using the total score. Using the Manitoba sample, initial psychometrics similarly indicated good generalizability across differences in tumour groups, scp, and location. Conclusions: We recommend the revised cpe-s and cpe-p for further use and development. Studies documenting the creation and standardization of scp evaluations are few, and we recommend further development of patient experience measures to improve both clinical practice and the specificity of research questions.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".