Integrating primary care and the cancer system: Program evaluation of the UPCON Network in Manitoba, Canada
Bibliographic record
Abstract
6605 Background: Cancer care facilities have generally been poorly integrated with primary care. CancerCare Manitoba (CCMB) created the UPCON (Uniting Primary Care and ONcology) Network to address this problem through partnerships with 13 primary care clinics in Winnipeg MB. A “Lead Family Physician (FP)” from each clinic was provided with focused CME and identified as a cancer resource to their colleagues. Each clinic was given access to the provincial electronic medical record for cancer patients and educational outreach undertaken with all clinic staff. Surveys were performed to assess the impact of UPCON on the perceptions of FPs of their working relationship with CCMB. Methods: The survey was adapted from other instruments, piloted and administered at baseline in 2004, and in 2005 and 2007 to 83 FPs in UPCON clinics and 228 other randomly selected FPs not involved in UPCON. A modified Dillman method was used and REB approval obtained. Results: Response rates were 44% in 2004, 32% in 2005 and 37% in 2007. UPCON FP respondents were less likely to be in fee-for-service practice and had fewer years in practice than the comparison group. Factor analysis revealed three scales with highly correlated items: 1) Information Flow (4 items, Cronbach alpha 0.79); 2) Avoiding Miscues (3 items, alpha 0.64); and 3) FP Role Satisfaction (5 items, alpha 0.64). There was a significant interaction between time and UPCON status for scales 2 and 3 (p= 0.027 and 0.0039) and a trend towards significance for scale 1 (p=0.060). These analyses were controlled for sex, FT/PT, method of payment, solo/group, and years in practice. When Lead FPs were excluded from the analysis, the interaction for scale 3 remained significant (p= 0.022). UPCON FPs were less satisfied at baseline for all three scales (p= 0.029, 0.072, 0.010) and equivalent to controls at follow-up. Conclusions: The Network had a positive impact over 3 years on UPCON FPs' perceptions but succeeded only in making their views similar to that of controls. UPCON FPs were more critical at baseline, perhaps due to “priming” during recruitment about collaboration problems or self-selection for involvement in this project. The results do not clarify which UPCON interventions in particular were effective. No significant financial relationships to disclose.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| 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".