Defining the term "underserved:" A scoping review towards a standardized description of inadequate access to veterinary services.
Bibliographic record
Abstract
This scoping review aims to establish a comprehensive definition of the term "underserved" as it applies to communities, individuals, and populations with inadequate access to animal health services, particularly for dogs. The review adhered to PRISMA guidelines and analyzed 30 articles, applying concepts of One Health and social determinants of health, by using 3 pre-determined categories of contributors to and indicators of underservice. The review categorized article-specific exemplars into veterinary-dependent barriers; community- and individual-related barriers; and health and welfare indicators; with subcategories illustrating features of underserved communities, individuals, or populations in each category. Ultimately, 3 definitions were developed. Animal Health Underserved Areas (AHUA) identify negative human and animal health and welfare outcomes secondary to inadequate access to animal health services in the community. Individuals may identify as underserved based on the same criteria (Animal Health Underserved Individuals, AHUI), and certain groups within otherwise adequately served areas may be identified as Animal Health Underserved Populations (AHUP). The AHUA, AHUI, and AHUP are frequently characterized as rural, remote, and/or Indigenous, and often face systemic marginalization. This inequitable access to animal health services creates human, animal, and community health challenges, underscoring the need for veterinary professionals and other stakeholders to prioritize equitable access to care. Findings from this review should inform development of a scoring system to enable comparative assessment of communities, individuals, and populations and allow strategic service and resource allocation in the future.
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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 | 0.047 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.036 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".