Interventions facilitating access to perinatal care for migrant women without medical insurance: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Inadequate pregnancy monitoring for pregnant migrant women without medical insurance (PMWMI) exposes them to severe complications during childbirth and consequences for the health of their child (e.g. preterm delivery, low birth weight, etc.). This scoping review aims to identify existing interventions globally to improve access to perinatal care for PMWMI. It will also highlight the strengths, weaknesses as well as the costs of these interventions. MATERIALS AND METHODS: The methodological framework developed by Arksey & O'Malley (2005) will be used. An electronic search of studies from 2000 to 2021, published in French or English, will be conducted in 12 databases. Publication in Websites of non-governmental organizations working on migrant women without medical insurance issues will be also searched. All articles related to perinatal follow-up and care of PMWMIs, regardless of design, will be included. Editorial comments will be excluded. Outcomes of interest will focus on the impacts, strengths, weaknesses, and cost of interventions. Selection of articles and data extraction will be done by two independent researchers following the Tricco et al. (2018) reporting guide. Finally, a deliberative workshop with experts will allow to identify the most promising and appropriate interventions that can facilitate access to perinatal services by PMWMIs in the Quebec province of Canada.
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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 | 0.066 | 0.052 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".