Integration between primary care providers and the cancer system: Gaps and opportunities
Bibliographic record
Abstract
6584 Background: A number of reports suggest that family physicians (FPs) are poorly integrated with the cancer care system. The specific gaps in care integration are poorly understood. In this study we examine specific processes of care associated with integration between FPs and regional cancer programs. Methods: Cross sectional survey of all identified primary care providers within a representative health region in Ontario, Canada. The survey instrument was created specifically for this study with items generated from published literature and expert input and pilot tested in a representative sample. A modified dilman method was used. Results: 500 physicians responded (response rate 60%). Overall 90% of respondants reported confidence in the workup of a new cancer case for the major disease sites but only half (54%) knew the process of referring to the regional cancer program. Only 57% felt investigations necessary could be done in a timely manner and 44% indicated that coordination of care needs to be improved. Most indicated preferance for an active navigation structure for newly diagnosed patients. Despite over 80% of respondents indicating use of the internet only 10% reported accessing cancer program web portals for information on the regional cancer program (such as waiting times). The majority of respondants (75%) indicated ongoing involvement in care during the active treatment phase, mostly for non cancer related medical issues but 20% indicated that they were not properly infomed of patients’ health status by the oncology program and only 57% indicated that they felt their role was valued by the cancer program during this phase in the care trajectory. In the follow up phase, 35% were unclear of their role specific to monitoring and surveillance. 60% felt their current compensation model was inadequate to support care of cancer patients. This did not vary by compensation model reported. Factors associated with better integration included attendance at educational sessions and years in practice. Conclusions: Cancer systems need to be more responsive to the needs of FPs to better integrate them and support optimal quality of care for cancer patients. Policies to clarify and support roles and responsibilites are necessary to ensure that FPs are integrated team members. 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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".