Evolving models of care for ambulatory systemic treatment.
Bibliographic record
Abstract
155 Background: Increasing demand for systemic cancer treatment in Ontario, Canada, due to an aging population and therapeutic advances, is compounded by resource shortages and provider burnout heightened by the COVID-19 pandemic. This landscape has led to longer wait times, reduced services, poorer patient experiences, and widened health inequities, particularly impacting marginalized populations. In response, Ontario Health (Cancer Care Ontario) developed recommendations to optimize ambulatory systemic treatment delivery from various perspectives, including patients, providers, cancer programs, and organizations. Methods: Evidence-based recommendations were derived from a targeted literature review, current state surveys, and follow-up interviews across 16 Ontario treatment facilities. Feedback was gathered via focus groups involving patients, families, care partners, and providers. Key informants from cancer agencies in Canada and internationally were consulted through jurisdictional scans. Furthermore, focused discussions with First Nations, Inuit, Métis, urban Indigenous (FNIMUI) communities, Francophone populations, and other equity deserving groups informed the unique needs, experiences, and barriers to care of these groups. Results: Twenty-six recommendations that span various aspects of systemic treatment delivery, including referral processes, scheduling, role integration, patient education, patient and provider experiences, virtual care, and care transitions were developed. Priorities include evidence-based, person-centred care, timely access, effective collaboration, provider well-being, and technological innovation. Additionally, we highlight partnerships with FNIMUI communities, and other equity-deserving groups as strategies to address additional barriers to care. Conclusions: These recommendations strive to address challenges faced by healthcare providers and patients, ensure equitable access to care, and improve provider well-being. Engagement with FNIMUI communities and other equity-deserving groups influenced these recommendations, emphasizing the need for system-level oversight and investment to realize equitable and sustainable service delivery province-wide.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".