Access to veterinary care: evaluating working definitions, barriers, and implications for animal welfare
Bibliographic record
Abstract
Humans have a moral obligation to meet the physical and mental needs of the animals in their care. This requires access to resources such as veterinary care, which is integral to achieving animal welfare. However, “access” to veterinary care is not always homogenous across communities and currently lacks a consistent definition. The objectives of this scoping review were to (1) understand how “access” to veterinary care has been defined in the literature, (2) map a broad list of potential barriers that may influence access to veterinary care, and (3) identify how access to care impacts the welfare of companion and livestock animals. The literature search yielded a total of 1,044 publications, 77 of which were relevant to our inclusion criteria, and were published between 2002 and 2022. Studies were most frequently conducted in the United States (n = 17) and Canada (n = 11). Publications defining access to veterinary care (n = 10) or discussing its impacts on animal welfare (n = 13) were minimal. However, barriers to accessing veterinary care were thoroughly discussed in the literature (n = 69) and were categorized into ten themes according to common challenges and keywords, with financial limitations (n = 57), geographic location (n = 35), and limited personnel/equipment (n = 32) being the most frequently reported. The results of this scoping review informed our proposed definition of access to veterinary care. Additionally, our findings identified a need to further investigate several understudied barriers relating to access to care (i.e., veterinarian-client relationship, client identity) and to better understand how they potentially affect animal welfare outcomes.
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 | 0.200 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.027 | 0.033 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".