Examining the experiences of Indigenous families seeking health information for their sick or injured child: a scoping review protocol
Bibliographic record
Abstract
Abstract Introduction The Truth and Reconciliation Commission drew attention to the inequalities and systemic harms experienced by Indigenous peoples in Canada and called on the Canadian government and healthcare professionals to close the gap related to Indigenous communities’ access to appropriate healthcare services. The Manitoba Métis Federation (self-governing organization representing Red River Métis) identified a need for Red River Métis families to have meaningful resources when seeking emergency care for their children. A better understanding of Métis families’ experiences in seeking child health information is needed to develop culturally relevant pediatric resources. To date, the literature on Indigenous families’ experiences seeking child health information has not been synthesized. A scoping review will map the literature on Indigenous families’ experiences seeking health information to care for a sick or injured child; and identify the barriers and facilitators to accessing this information. Methods and analysis Joanna Briggs Institute methodology was used to develop the research question, What is the extent and nature of the literature available on the experiences of Indigenous families seeking health information for their sick or injured child? The search strategy, developed with a research librarian with extensive experience in Indigenous Peoples’ health, includes searching MEDLINE, EMBASE PsycINFO, CINAHL, and Scopus databases; grey literature, by searching the internet and consulting reference lists of key publications; examining key Indigenous research journal articles not indexed in the major biomedical databases; and snowball sampling. Two independent reviewers will screen titles and abstracts against the inclusion criteria, then screen the full texts of selected citations. Data will be extracted, collated and charted to summarize the types of studies, healthcare contexts, health information accessed, how health information was accessed, barriers and facilitators to accessing information and related measures. Ethics and Dissemination A consultation exercise with a community advisory committee will review results and inform future research. Results will be integrated with findings from other project stages to inform the adaptation of a child health resource for Red River Métis families.
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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 | 0.124 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".