Increasing Diversity and Equity in the Health Services Research Workforce: A Systematic Scoping Review
Bibliographic record
Abstract
Objective: The objective of this scoping review is to identify, describe, and evaluate practices that promote the hiring, promotion, and retention of employees from underrepresented groups in the health services research (HSR) workforce. This review will systematically map the research, programs and interventions implemented among HSR organizations in the U.S. and Canada in the past 10 years and identify any knowledge gaps as well as emergent initiatives. Introduction: HSR institutions build the evidence base that informs debate and decision-making by policymakers and academic research and health system leaders. Yet studies show that underrepresented groups have not been encouraged to join or stay in the research enterprise. Thus, is it incumbent upon HSR to identify and highlight recent initiatives and practices that have reduced barriers and promoted the hiring, promotion and retention of diverse and marginalized contributors in the research field. Inclusion criteria: Articles will be peer-reviewed; written in English; published between Jan 1, 2012 and Jan 19, 2022; and focused on initiatives, programs and practices in U.S. and Canada to build and sustain HSR workforce diversity. We define “workforce” to include: faculty, research staff and assistants, medical residents (if involved with research) and post-doctoral fellows. Publications are excluded if they simply describe diversity (or lack of diversity); or focus on students (e.g., high school, college, graduate or medical school) or on study participants or subjects. Underrepresented groups were defined based on race/ethnicity, religion, socioeconomic status, age, disability status, gender identity, and sexual orientation. Methods: We will conduct a search of the literature using two electronic bibliographic databases: PubMed and Embase. To reserve the review for contemporary initiatives in hiring, promotion, and retention of employees from underrepresented groups in the health services research (HSR) workforce, we will apply search limits (See Inclusion Criteria). Following the Arksey and O’Malley approach [1], we will conduct a systematic scoping review guided by the PRISMA-ScR checklist (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) [2]. References 1. Arksey H, O'Malley, L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. 2. Tricco AC, Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., ... & Straus, S. E. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of internal medicine. 2018;169(7):467-473.
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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 | 0.119 | 0.362 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".