Optimizing Equitable Cancer Diagnoses in Canada
Notice bibliographique
Résumé
Short introduction/background summary that is understandable to the readers who do might not be familiar with the context: Why did you do it? Please explain the problem and the context. While earlier cancer diagnoses mean better outcomes, cancer diagnosis is full of delays, often driven by service and information fragmentation. All. Can Canada learns about the current state, outcomes patients want to achieve for a better future, and policies plus practices that help move from today delays to tomorrow early diagnoses. Who is it for? Please explain what community or people you are targetingPrimary care providers (including family physicians, nurse practitioners, and allied health team members such as nursing, pharmacy, social work, psychology) especially those who are part of primary care/primary health care teams Labs/pathology/radiology/oncology specialists/navigators/psychosocial support professionals, especially members of dedicated cancer investigation teams. People working on cancer diagnosis solutions like pathways, integrated practice units, AI risk assessment tools, within research, government, and innovation industries Health data experts that focus on real-time, shareable health information to improve patient experience and outcomes. Diverse people with lived experience of a cancer diagnosis (patients and caregivers) Who are you involving and engaging with? Please explain who is involved in the design, implementation and monitoring of the initiative. We are especially interested in Personal and Public Involvement and co-design approaches. Be specific. This information is essential. Led by patient groups and people with lived experiences of cancer, All.Can Canada (ACC) is a national, multi-stakeholder network for cancer care efficiency, with a preliminary aim of optimizing people entry into cancer care through swift, accurate, and appropriately delivered diagnosis. The sectors that are voting members of the ACC network include patients and caregivers with lived experience of cancer, patient groups, family physicians, pharmacists, oncologists, researchers, and policymakers. Anyone from the cancer diagnosis ecosystem in Canada, especially people with lived expertise, are welcome as network members. Thus far, we have engaged more broadly with nurse navigators, psychosocial oncology practitioners, some jurisdictional decision-makers, and health data stakeholders involved in improving the Canadian health data ecosystem through interoperable health data. We particularly seek to deepen our engagement with primary care associations, especially those representing family physicians and nurse practitioners working in primary care. What are you doing or propose to do? Please explain the initiative or intervention. If this is not yet clear and you want to make this point of discussion, make this point. ACC has created a national network for earlier cancer diagnoses. Inspired by Collective Impact and the Constellation Governance Model, it welcomes anyone from the cancer diagnosis ecosystem, especially people with lived expertise of cancer, to work together. Our common agenda is defined by mixed methods research we undertook and continue to refine, with many key findings resulting from cancer patient and caregiver interviews. We learn about earlier cancer diagnoses, especially from the perspective of diverse people with lived expertise. This year, we have deepened our understanding of issues and opportunities related to structurally underserved communities. From that evolving knowledge base, we build relationships with relevant stakeholders and share what we are learning with them to support their success and ability to improve earlier cancer diagnoses for everyone in Canada. Is the question you want to ask international colleagues or problem you want their help to solve? In the context of Canada fragmented healthcare systems, how can integrated and equitable cancer diagnoses become the systemic norm across the country for all parts of the population, including structurally underserved communities, rather than exceptional pockets of excellence?
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,008 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».