Spatial models of access to health and care services in rural and remote Canada: a scoping review protocol
Bibliographic record
Abstract
Objective: The objective of this review is to determine the scope of spatial modelling approaches used to evaluate geographic access to health and care services in rural Canada. Introduction: Canada’s health and social policy agenda has made the requirement for equal access to primary and secondary health services for rural populations a key priority. Most rural health research in Canada has focused on measuring patterns of health outcomes or modelling geographic access to a narrow range of services, health conditions, or within specific regions. This scoping review will provide an in depth look at the spatial modelling currently being used to evaluate the barriers and facilitators for access to health and care services and will provide direction for further research. Inclusion criteria: This review will consider studies that include any person accessing health and care services in Canada, focusing on those who reside in rural or remote communities, or access health services in those areas. Methods: Published primary studies, reviews, opinion papers, reports, theses, and dissertations published in English or French across all dates will be searched in databases including CINAHL via EBSCO, PubMed, ProQuest, Scopus, Web of Science and Dissertations and Theses Global. Following the search, all titles and abstracts will then be assessed against the inclusion criteria for the review. Potentially relevant papers will be assessed in detail against the inclusion criteria. The data extracted will include geographic location, service under study, analytic methodology, data included, and specifics of the spatial models employed.
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 | 0.091 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".