Canadian medical oncology workforce and cancer incidence trends: Supply and proxy demand analyses from 1994 to 2020.
Notice bibliographique
Résumé
e13526 Background: It is unclear whether the Canadian medical oncology workforce has kept pace with the country’s rising cancer incidence over the last three decades. This study sought to characterize the national and provincial trends in the medical oncologist (MO) and MO trainee workforce during the last 27 years and explore the relationship between workforce and cancer incidence. Methods: Publicly available databases from the Canadian Medical Association (CMA; 1994-2019) and Canadian Institute of Health Information (CIHI) database (1994-2020) were utilized to estimate the number, demographics, and regional distribution of practicing MOs in Canada from 1994 to 2020. Cancer incidence by province was obtained from Statistics Canada for the period 1990 to 2018, except for Quebec where only 1990-2010 data was available, and then projected to 2020 across five regions (The West Coast, The Prairies, Ontario, Quebec and Atlantic Canada) using Canadian population statistics from Statistics Canada and age-period-cohort modeling. To estimate changes in demand for, and supply of, medical oncology services over time, annual cancer incidence to MO provider ratios were calculated. Finally, the Canadian Post MD Education Registry (CAPER) database (1994-2020) was used to characterize the number of MO trainees. Results: Between 1994 and 2020, annual cancer incidence nationally rose from 120,255 cases to 225,800 cases (88%), while the number of MOs increased from 161 to 642 (298%). Incident cancer cases to medical oncologist (MO) ratio dropped from 749:1 in 1994 to 352:1 in 2020. This ratio fell in all five analyzed regions from 1994 to 2020 (Atlantic Canada 1836:1 to 447:1, The Prairies 1257:1 to 377:1, The West Coast 909 to 261:1, Ontario 682:1 to 411:1 and Quebec 543:1 to 300:1). In 1994, 24% of MO providers were ≥ 50 years old compared with 40% in 2020. Nationally, the MO workforce has nearly reached gender parity with 46% female in 2020 versus 25% in 1994. Trends in Canadian MO trainees have largely mirrored MO trends with a 203% increase in the annual trainee cohort from 34 in 1994 to 103 in 2020. In 1994, 34% of trainees were female compared with 63% in 2020. The largest proportion of the country's MOs (34% in 2020) and MO trainees (49%) are located in Canada’s most populous province, Ontario. Conclusions: Although the Canadian MO workforce has shown considerable growth between 1994-2020, a higher proportion of MO providers are nearing retirement age and may influence future workforce trends. Moreover, our study was not able to take into consideration referral patterns or the increasing number and complexity of systemic therapies that also influence MO workload. Ongoing monitoring of human resource levels in medical oncology and cancer incidence data are key metrics to meaningfully inform MO training programs of an appropriately sized trainee cohort and ensure future demands for MO services in cancer care are met.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,008 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».