Ontario Medical Association

Soil health assessment is new to farmers in Ontario, Canada, and is theorized as a tool for increasing the adoption of beneficial management practices (BMPs). The Greenbelt Foundation and Soil Health Institute are pilot testing how to make soil health assessments and recommendations available to grain and oilseed farmers in Ontario’s Golden Horseshoe region (2023–2026). The program provides soil health tests measuring indicators such as soil organic matter, aggregate stability, and carbon mineralizable potential. Farmers receive a detailed soil health report with results, locally relevant comparisons, and suggestions of practice changes, followed by conversations with a Certified Crop Advisor (CCA) to guide BMP adoption. Over 100 farmers participated in the first two years, with soil samples taken on 162 cropland fields. Initial results show that providing soil health tests with a detailed report leads to more farmers implementing and planning future BMPs. Participants were willing to try new practices, expand crop rotations, reduce tillage, and plant cover crops. Meeting with the program's CCA helped farmers overcome barriers by providing education and connecting them with cost-shared programs. To date, 8000 acres of new BMPs have been implemented, demonstrating the benefits of combining soil health testing, tailored management plans, and extension support.

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into healthcare, offering potential advancements in patient-reported outcome measures (PROMs) for surgical populations. Improved PROMs can enhance patient-centered care by accurately capturing patient experiences with minimal burden. In the context of surgery, where recovery trajectories vary widely, this study aims to systematically review the use of AI and ML in the development, application, and prediction capabilities of PROMs in surgical populations, with a focus on psychometric properties and the predictive accuracy of post-surgical outcomes. A comprehensive search of the PubMed database was conducted from inception until August 2024. Studies were included if they utilized AI or ML in the development, application, or predicting PROMs for surgical patients. Methodological quality was assessed using COSMIN and PROBAST tools, depending on study design. A qualitative synthesis of findings was performed. Twenty-two studies met the inclusion criteria, with 19 rated as high quality. Six studies focused on developing computer adaptive tests (CAT) PROMs, seven on evaluating psychometric properties, and five on ML for post-surgical outcome prediction. CAT PROMs showed comparable measurement accuracy to traditional PROMs, good to excellent construct validity, and significantly reduced patient burden by reducing the length of questionnaires. ML algorithms, such as logistic regression, random forests, extreme gradient boosting, and neural networks, achieved similar predictive accuracy for post-surgical outcomes, with no single model demonstrating consistent superiority. AI and ML have the potential to improve PROM utilization in surgical care by enhancing efficiency and personalization while maintaining data quality. Clinicians can use AI-driven PROMs to reduce patient burden and integrate ML models for accurate post-surgical outcome prediction, thereby optimizing patient-centered care.

BackgroundDigital conversational agents (or “chatbots”) that can generate human-like conversations have recently been adapted as a means of administering mental health interventions. However, their development for youth seeking mental health services requires further investigation.ObjectiveThis youth-engaged scoping review synthesizes the recent research on digital conversational agents for youth seeking mental health or substance use services.MethodsStudies were included if they were published between 2016 and 2025 and examined digital conversational agents for youth aged 11 to 24 years with mental health or substance use challenges in clinical settings. Systematic literature searches were conducted in February 2024 in multiple databases and updated in March 2025. Data were extracted using codeveloped forms and synthesized narratively.ResultsTen studies were included, all focusing on mental health. Seven examined the acceptability and feasibility of digital conversational agents; others explored youth perceptions of use, design, and content, with some exploration of impact on mental health symptoms. Eight of ten studies reported high acceptability or positive user experiences. Three were randomized controlled trials that found potential reductions in depressive symptoms. Reporting on the ethical standards was limited. No studies focused on substance use alone.ConclusionsLiterature on digital conversational agents for treatment-seeking youth is emerging but limited. Future rigorous research is needed that prioritizes data security, safety measures, and youth co-design in the development of safe, engaging, digital conversational agents for youth with mental health conditions.

ObjectivesTo determine how high performing is defined in relation to a health system and chart the literature on the definitions and key concepts of high-performing healthcare systems.DesignScoping review.Data sourcesMEDLINE, EMBASE, CINAHL and the Cochrane Central Register of Controlled Trials were searched from inception to July 2024. The grey literature was also searched.Eligibility criteriaIncluded studies reported on health systems and high performance to identify explicit definitions, research outcomes and knowledge gaps.ResultsTwo reviewers independently screened 5721 citations and 507 full-text articles, resulting in the inclusion of 35 primary articles and 47 companion documents in the review. Three independent definitions for a high-performance health system were identified. 24 research studies reported outcomes on the elements of a high-performing health system (58%), system evaluation (32%) and tool development or validation (10%). Knowledge gaps identified were the lack of a common definition, a lack of common indicators, strategies for moving evidence into policy and practice, and difficulties with comparisons across health systems.ConclusionsWe found limited definitions and a lack of empirical evidence on our topic. There is an opportunity for primary research in the area of health systems and high performance.

<h3>Objectif</h3> Contribuer à la planification des effectifs en actualisant les tendances dans les caractéristiques des médecins de famille (MF) en soins complets à l’approche de la retraite et celles de leurs patients depuis la pandémie de la COVID-19. <h3>Type d’étude</h3> Une analyse transversale en série à l’échelle de la population au moyen d’ensembles de données administratives reliées entre elles sur la santé. <h3>Contexte</h3> L’Ontario. <h3>Participants</h3> La population de l’Ontario au 31 mars 2022 (15 023 570) et les MF en soins complets à qui ils sont rattachés (9375). Nous avons comparé ces populations aux analyses prépandémiques (2008, 2013 et 2019). <h3>Principaux paramètres à l’étude</h3> Les tendances temporelles dans le nombre, la proportion et les caractéristiques des MF en soins complets; les MF en soins complets à l’approche de la retraite; et les patients rattachés à des MF en soins complets, en insistant sur les MF proches de la retraite. <h3>Résultats</h3> Après 2019, la croissance dans les effectifs globaux de MF en soins complets a stagné (2019 : 9377; 2022 : 9375). Pour la première fois durant la période à l’étude, soit en 2022, il s’est produit une baisse du nombre et de la proportion de médecins en début de carrière (âgés de &lt;35 ans), et les femmes médecins en formaient la majorité (51,5 %) des effectifs. Une proportion grandissante des effectifs est âgée de 65 ans et plus (2008 : 10,0 %; 2013 : 14,4 %; 2019 : 13,9 %; 2022: 15,2 %), et, proportionnellement, un nombre et une proportion à la hausse de patients sont rattachés à des MF à l’approche de la retraite. La cohorte de MF la plus âgée (âge de ≥70 ans) a aussi augmenté en nombre et en proportion en 2022. Les patients rattachés à des MF proches de la retraite étaient plus âgés et souffraient de niveaux plus élevés de problèmes chroniques par rapport aux patients dans l’ensemble des effectifs de MF. Les tailles moyennes de la patientèle sont restées relativement stables et, de manière uniforme, les femmes MF soignaient de plus petites patientèles que les hommes MF. Dans une proportion grandissante, les patients avaient le degré le plus élevé de complexité, et les pratiques dans tous les groupes d’âge de MF comptaient des proportions grandissantes de ceux ayant de plus grands besoins de ressources. <h3>Conclusion</h3> Depuis la pandémie de la COVID-19, les changements dans les effectifs de MF en soins complets, combinés à la complexité accrue des patients, soulèvent des inquiétudes quant à la capacité des effectifs d’absorber les patients dont les MF sont sur le point de prendre leur retraite.

ABSTRACT This is a discussion of “Permeable boundaries: Therapeutic benefits,” by Geist and Nocek. In my opinion, the core of their paper is the mutuality of their love and trust for each other, and they use the term “permeable boundaries” as a metaphor for these feelings, rather than it being the kind of singular, relational state they are proposing. As a positive contribution, the experience-near description of their mutual love for each other expands the degree of intimacy in psychoanalytic writing about analytic love. However, there is a lack of sufficient information to understand their paper as a psychoanalytic treatise, and I have a number of disagreements with their conceptualization of “permeable boundaries,” as well as concerns about the quality of their mutual love which they portray as idyllic but which, in my opinion, lacks certain key elements of analytic love.

Underemployment is a reality for many new graduates, who accept locum or part-time work as an alternative to unemployment because of lack of opportunities. We sought to analyze orthopedic surgeons' Ontario Health Insurance Program (OHIP) billing data over a 20-year period as a proxy of practice patterns and hypothesized that billing in the first 6 years of practice would be affected by underemployment and locum. We analyzed the annual average billing totals of orthopedic surgeons, broken down by year of graduation, year of billings, and number of surgeons billing in that year. We analyzed public census data of the Ontario population size as a proxy of orthopedic demand. A 2019 cross-sectional analysis showed that around 15 surgeons per graduating year were billing in Ontario from the 1995 to 2016 cohorts, while 2017 and 2018 saw an increase to 30 and 36 actively billing surgeons, respectively. The number returned to more historical numbers in 2019, with 20 actively billing surgeons. For those surgeons billing in Ontario, billing trends have been roughly stable, with average billings increasing each year for the first 6 years in practice (p < 0.001). Year of graduation did not have an effect on the first 6 years of billings (p > 0.5). Billings were stable after 6 years in practice (p > 0.09). The Ontario health care system has not expanded to support more orthopedic surgeons despite the aging and growing population; despite our growing population, the number of surgeons being trained and retained has not matched this growth. Further research needs to be done to guide optimal health human resource decision-making.

&lt;p&gt;&lt;strong&gt;Introduction&lt;/strong&gt; Cow’s milk is a dietary staple for children in North America. Though clinical guidelines suggest children transition from whole (3.25% fat) milk to reduced (1% or 2%) fat milk at age 2 years, recent epidemiological evidence supports a link between whole milk consumption and lower adiposity in children. The purpose of this trial is to determine which milk fat recommendation minimises excess adiposity and optimises child nutrition and growth.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Methods and analysis&lt;/strong&gt; Cow’s Milk Fat Obesity pRevention Trial will be a pragmatic, superiority, parallel group randomised controlled trial involving children receiving routine healthcare aged 2 to 4–5 years who are participating in the TARGet Kids! practice-based research network in Toronto, Canada. Children (n=534) will be randomised to receive one of two interventions: (1) a recommendation to consume whole milk or (2) a recommendation to consume reduced (1%) fat milk. The primary outcome is adiposity measured by body mass index z-score and waist circumference z-score; secondary outcomes will be cognitive development (using the Ages and Stages Questionnaire), vitamin D stores, cardiometabolic health (glucose, high-sensitivity C-reactive protein, non-high density lipoprotein (non-HDL), low density lipoprotein (LDL), triglyceride, HDL and total cholesterol, insulin and diastolic and systolic blood pressure), sugary beverage and total energy intake (measured by 24 hours dietary recall) and cost effectiveness. Outcomes will be measured 24 months postrandomisation and compared using analysis of covariance (ANCOVA), adjusting for baseline measures.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Ethics and dissemination&lt;/strong&gt; Ethics approval has been obtained from Unity Health Toronto and The Hospital for Sick Children. Results will be presented locally, nationally and internationally and published in a peer-reviewed journal. The findings may be helpful to nutrition guidelines for children in effort to reduce childhood obesity using a simple, inexpensive and scalable cow’s milk fat intervention.&lt;/p&gt;