Comparative study of the perceptions of continuity of care of CRC survivors transitioned to primary care.
Bibliographic record
Abstract
96 Background: Colorectal cancer (CRC) patients are increasingly being discharged to a primary care provider (PCP) for follow-up care. This study used a pre-post design to evaluate the impact of the Moving Forward After Cancer(MFAC) program at CancerCare Manitoba on patient perceptions of continuity of care. Methods: The MFAC program was introduced in 2010 to support transfer of care to primary care and includes transitional appointments (TAs) and survivorship care plans. The pre-implementation (PRE) group was a random sample of patients on the provincial cancer registry diagnosed in 2008/09 with Stage II / III CRC. The post-implementation (POST) group was recruited in oncology clinics after their TA over four years starting in 2010. Both groups completed the same mailed survey. Respondents were asked to identify the main provider(s) of their follow-up care. Those indicating a PCP completed the Patient Continuity of Care Questionnaire (PCCQ) which assesses continuity upon discharge with scoring between 5 and 25. Quality of life, disease, treatment, and demographic data were collected. Results: There were 246 responses in the PRE group (rate of 68.3%) and 83 (66%) in the POST. The POST group was significantly younger, more urban and further from diagnosis. It included more Stage III and rectal cancer patients who received more treatment. Comparing the subset of both groups (106 vs 65) who described a PCP as a main provider, the POST group demonstrated higher scores on the PCCQ (24.0 v 22.8, p=0.0065) and on two of its subscales. This finding persisted when tested for the impact of differences between the two groups in stage, site, age and treatment. The most common pattern of provider involvement in follow-up was the “FP alone,” which rose from 19% in the PRE group to 54% in the POST (p<0.0001). The proportion of participants who felt “adequately prepared” for their transfer of care was significantly increased (71 vs 91%, p=0.003). Conclusions: A formal information sharing process with CRC patients at time of transition to primary care follow-up improved their evaluation of continuity of care. Significant shifts were seen in the locus of care from oncology to primary care settings consistent with the intent of the MFAC program.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".