Protocol for a scoping review of the qualitative literature on Indigenous infant feeding experiences
Bibliographic record
Abstract
INTRODUCTION: Prudent infant nutrition, including exclusive breastfeeding to 6 months, is essential for optimal short-term and long-term health. Quantitative research to date has documented that many Indigenous communities have lower breastfeeding rates than the general population and that this gap in breastfeeding initiation and maintenance may have an important impact on chronic disease risk later in life. However, there are critical knowledge gaps in the literature regarding factors that influence infant feeding decisions. Qualitative research on infant feeding experiences provides a broader understanding of the challenges that Indigenous caregivers encounter, and insights provided by this approach are essential to identify research gaps, community engagement strategies, and programme and policy development. The objective of this review is to summarise the qualitative literature that describes breastfeeding and other infant feeding experiences of Indigenous caregivers. METHODS AND ANALYSIS: This scoping review will follow guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews, the Joanna Briggs Institute and the methodological framework from Arksey and O'Malley. In October 2020, we will conduct an electronic database search using Medline, Embase, The Cumulative Index to Nursing & Allied Health Literature (CINAHL), PsycINFO, and Scopus, and will focus on qualitative studies. Publications that have a focus on infant feeding in Canada, the USA, Australia and New Zealand, and the Indigenous caregiver experience from the caregiver perspective, will be included. We will conduct a grey literature search using Indigenous Studies Portal, country-specific browser searches, and known government, association, and community websites/reports. We will map themes and concepts of the publications, including study results and methodologies, to identify research gaps, future directions, challenges and best practices in this topic area. ETHICS AND DISSEMINATION: Ethical approval is not required for this review as no unpublished primary data will be included. The results of this review will be shared through peer-reviewed publications and conference presentations. This protocol is registered through the Open Science Framework (osf.io/4su79).
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 | 0.142 | 0.146 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.174 | 0.036 |
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".