Defining and measuring health equity effects in research on task shifting interventions in high-income countries: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Task shifting interventions are intended to both deliver clinically effective treatments to reduce disease burden and address health inequities or population vulnerability. Little is known about how health equity and population vulnerability are defined and measured in research focused on task shifting. This systematic review will address the following questions: Among task shifting interventions in high-income settings that have been studied using randomised controlled trials or variants, how are health inequity or population vulnerability identified and defined? What methods and indicators are used to describe, characterise and measure the population's baseline status and the intervention's impacts on inequity and vulnerability? METHODS AND ANALYSIS: Studies were identified through database searches (MEDLINE, Embase, CINAHL, PsycINFO and Web of Science). Eligible studies will be randomised controlled trials published since 2004, conducted in high-income countries, concerning task shifting interventions to treat any disease, in any population that may face health disadvantage as defined by the PROGRESS-Plus framework (place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, social capital, socioeconomic position, age, disability, sexual orientation, other vulnerable groups). We will conduct independent and duplicate title and abstract screening, then identify related papers from the same programme of research through further database and manual searching. From each programme of research, we will extract study details, and definitions and measures of health equity or population vulnerability based on the PROGRESS-Plus framework. Two investigators will assess the quality of reporting and measurement related to health equity and vulnerability using a scale developed for this study. A narrative synthesis will highlight similarities and differences between the gathered studies and offer critical analyses and implications. ETHICS AND DISSEMINATION: This review does not involve primary data collection, does not constitute research on human subjects and is not subject to additional institutional ethics review or informed consent procedures. Dissemination will include open-access peer-reviewed publication and academic conference presentations.PROSPERO Registration Number CRD42017049959.
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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 | 0.147 | 0.164 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.023 | 0.021 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".