Quantifying the Impact of Family Doctors on the Care Experiences of Patients with Cancer: Exploring Evidence from the 2021 Ambulatory Oncology Patient Satisfaction Survey in Alberta, Canada
Bibliographic record
Abstract
Oncology programs across Canada are reaching capacity as more Canadians are diagnosed with and treated for cancer each year. There is an increasing need to share care with family doctors, however it is unclear how this type of care impacts patient experiences, particularly while receiving active treatment. Retrospective data from the 2021 Ambulatory Oncology Patient Satisfaction Survey (AOPSS) in Alberta, Canada was used in this study. A unique question on the Alberta survey asks patients about their family doctor's involvement during their cancer care. Patient satisfaction across the six domains of person-centred care on the AOPSS was analyzed based on how involved a patient's family doctor was. Compared to patients who indicated their family doctor was "Not involved", patients with "Very involved" family doctors had significantly higher satisfaction scores in all six domains of care. The three domains which showed the largest positive impact of family doctor involvement were: Coordination & Integration of Care, Emotional Concerns, and Information, Communication & Education. The results demonstrate that involving family doctors in cancer care can be beneficial for patients. Based on the observed satisfaction increases in this study, shared care models may be preferred by many patients. These models of care can also help alleviate strain and capacity issues within cancer programs. The results could be used to support recommendations for cancer care teams to regularly involve and communicate with family doctors, to ensure that patients receive comprehensive and tailored care from all their health care providers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".