Rural health care providers’ experiences of providing care to children and youth with disabilities: a scoping review
Bibliographic record
Abstract
BACKGROUND: Health care providers face many challenges with delivering care in rural areas. However, little is known about their experiences of providing care to marginalized populations, such as children with disabilities. The purpose of this study was to explore the experiences of health care providers serving rural children with disabilities. METHODS: We conducted a scoping review following the Joanna Briggs Institute methodology and an inductive thematic approach involving eight international databases (Embase, CINAHL, JBI EBP, Healthstar, Ovid Medline, PsycINFO, Scopus, Web of Science). Of the 2631 screened articles, 31 met the inclusion criteria. RESULTS: The studies spanned across seven countries over a 24-year period. Our findings involved the following themes: (1) training and knowledge of childhood-onset disabilities (i.e., lack of training; adequate training); (2) communication (i.e., communication concerns; effective communication and rapport building with local communities); and (3) lack of resources, services and supports (i.e., staff shortages; travel and accessibility issues; challenges of intersectional needs of rural patients). CONCLUSIONS: Our findings emphasize the extent of the challenges that rural health care providers encounter in delivering care to children with disabilities. There is a critical need for further supports, resources and targeted interventions to recruit and retain rural health care providers.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".