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For more than 30 years, the Manitoba Centre for Health Policy has been conducting research and evaluation to provide timely and critical evidence to answer real-world policy questions. Our experienced team of research scientists, analysts and other staff work extensively with policy-makers at the macro, meso and micro levels of government to support evidence-informed policy and program development in an effort to ensure that policy initiatives provide the greatest benefit possible to individuals and society as a whole. Using the widely recognized whole-population Manitoba Population Research Data Repository, which comprises approximately 100 different datasets from multiple sectors, we employ sophisticated and state-of-the-art research methods and data science technologies, and then translate the results into meaningful insights or recommendations for policy-makers. Our long and productive history of working with policy-makers has taught us much about making our research relevant to policy-makers. In this article, we outline some examples of how research evidence has been used to influence policy in Manitoba, and the key lessons we have learned about what makes relationships between researchers and policy-makers work. In essence, policy-makers have supported the growth of the Repository over the last 30 years, because researchers have "closed the loop" by sharing valuable and policy-relevant research results with them. This ability to inform policies, programs and service delivery with scientific evidence continues to benefit individuals, communities and our society as a whole.

IntroductionUsing data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.
MethodsWe adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.
ResultsThe initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.
ConclusionOur review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.

We explored the particularities of the Healthy Baby Prenatal Benefit (HBPB), an unconditional cash transfer program for low-income pregnant women in Manitoba, Canada, which aims to connect recipients with prenatal care and community support programs, and help them access healthy foods during pregnancy. While previous studies have shown associations between HBPB and improved birth outcomes, here we focus on how the intervention contributed to positive outcomes. Using a case study design, we collected data from government and program documents and interviews with policy makers, academics, program staff, and recipients of HBPB. Key informants identified using evidence and aligning with government priorities as key facilitators to the implementation of HBPB. Program recipients described how HBPB helped them improve their nutrition, prepare for baby, and engage in self-care to moderate the effect of stressful life events. This study provides important contextualized evidence to support government decision making on healthy child development policies.

This study, a partnership between <Organization 1> and <Organization 2>, investigated whether attending full-time kindergarten (FTK) was associated with better educational outcomes for Metis students in Manitoba, who may face unique barriers to academic success. We utilized linked administrative data from the Manitoba Population Research Data Repository. For each measured education outcome, there were no significant differences in how Metis students who attended FTK vs Metis students who attended half-time kindergarten (HTK) performed. FTK does not provide sufficient support to Metis students to overcome the structural barriers to academic success they may face. It is likely that an upstream approach to addressing the structural barriers is needed to support improved outcomes in this population.

Problem: In Canadian society, public policies guide the development and administration of social services and systems, including the public education system, the justice system, family services, social housing and income support. However, because social services are often planned and implemented in a 'siloed' manner, coordination and collaboration across departments, sectors and organisations is sorely lacking. Data and resource constraints may prevent services being evaluated to ensure they meet the needs of the people for whom they are intended. When the needs of individuals are not addressed, the result is poor outcomes and wasted resources across multiple areas.Our Response: In 2018, we formed the SPECTRUM Partnership in response to a recognised need for collaborative cross-sector approaches to strengthening the policies that shape social services and systems in our country. The tripartite SPECTRUM partnership comprises representatives from community organisations, government and academia, and is an entity designed to conduct social policy research and evaluation, incorporating interdisciplinary perspectives and expertise from its members. Guided by community-driven research questions and building on existing data resources, SPECTRUM seeks to address specific knowledge gaps in social programs, services and systems. New research findings are then translated into viable public policy options, in alignment with government priorities, and presented to policy-makers for consideration.Implications: In this practice-based article, we describe the key steps we took to create the SPECTRUM partnership, build our collective capacity for research and evaluation, and transform our research findings into actionable evidence to support sound public policy. We outline four of SPECTRUM's achievements to date in the hope that the lessons we learned during the development of the partnership may serve as a guide for others aiming to optimise public policy development in a collaborative evidence-based way.