Driving Equity at a Community Level: Case Studies of Community-Based Peer-Delivered Health-Care Services and Programs
Bibliographic record
Abstract
IntroductionThe Wellesley Institute, based in Toronto, Ontario, Canada is a non-profit and non-partisan research and policy institute focused on developing research and community-based policy solutions to problems of urban and disparities. Wellesley commissioned this Case Study Series, Driving Equity at a Community Level: Case Studies of Community- Based Peer-Delivered Health-Care Services and Programs, in 2010 to complement a Literature Review it conducted, Potential of Community-Based Peer-Delivered Healthcare Services and Programs (1). The series was envisioned in several phases, with Phase 1 focusing on peer workers in programs and services located in Greater Toronto Area (GTA).Interest in peer workers, and evidence of their practice and its outcomes among and social service and their clients in Greater Toronto Area, developed out of a series of Health Equity Roundtables facilitated by The Wellesley Institute in 2009. These Roundtables brought together about 30 service providers, policymakers, and community- and academic-based researchers as informants to create an informal best practices and advocacy network in GTA around disparities or inequities, socio- economic determinants of health, promising directions and gaps, and enablers and barriers to change.The emerging philosophy and practice of peer workers was one area of discussion. At suggestion of Roundtables, The Wellesley Institute collaboratively developed this project to dig deeper into how and why peer workers provide and social services in GTA through a parallel and complementary Literature Review/Case Study approach that looks at three key lines of experiential inquiry: how peer-based program or service works, why it works (facilitators, success conditions or best practices), and what challenges working with this model presents (barriers). This paper reports on Phase 1 of project.Two notes about terms and definitions used in this project. Peer is a flexible term as currently used in both literature and in practice. This project adopts definition suggested by literature: the term 'peer' is defined loosely as someone from being served. Such a loose definition allows for varying levels of expertise, from laypersons to professionals, so long as person possesses identifying traits of community (1). Similarly, when speaking of community, literature suggests that:'Community' refers to a group of people sharing identifying common traits such as ethnicity, race, religion, location or neighbourhood, sexual orientation, past or present concern, educational status, age, lifestyle, and life-stage. To be community-based, a service or program must take place as close to as possible (1).Where necessary, this paper refers to broader term health and social service providers to reflect reality that and social service sectors do cross over, both in policy and practice; and that many service are, in fact, multi-service agencies that work across sectors, disciplines and areas of practice. Several of eight specific programs or services studied are multi-service agencies.A comment about project's scope. The terms of reference were highly focused for several reasons. The project had limited resources to access and collect data among communities of practice whose existence, location, size and client reach were relatively unknown among and social service in GTA, or known primarily through ever-changing networks of community-based contacts.As a result, Phase 1 was envisioned as a preliminary, exploratory or pilot research project, a starting-point to explore discussions, promising directions and recommendations from Health Equity Roundtables and suggest next steps for Phase 2. The case study findings reported in this paper are therefore based on data gathered from a relatively small number, eight, of community-based peer- delivered healthcare services and programs that constitute field work component of project. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".