Uncovering the state of knowledge about healthcare gentrification: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Healthcare gentrification is the process in which the distribution of healthcare resources within a neighbourhood affects residents' access to healthcare services. To understand the complexity of healthcare access and to consider the socio-structural dimensions affecting equity in access to care, we aim to explore how healthcare gentrification has been described in the scientific literature and to document the reported relations between gentrification and healthcare access. METHODS AND ANALYSIS: (2010). We will search the following databases: MEDLINE (OVID), Embase (embase.com), CINAHL Plus with Full Text (EBSCO), Web of Science and Geobase (Engineering Village). The review will be conducted from February 2024 to September 2024. The search strategy will be elaborated in conjunction with a professional librarian. Screening of titles and abstracts and full-text screening will be done in duplicates. A third reviewer will arbitrate discrepancies during the screening process. We will present our results narratively. ETHICS AND DISSEMINATION: This scoping review does not require ethical approval since it will be collected from publicly available documents. The results of this scoping review will also be presented as a scientific article, scientific conferences, research webinars also in social media, workshops and conferences organised by healthcare organisations or academic institutions or on any appropriate platform.
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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 | 0.180 | 0.158 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 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".