Access to veterinary care: evaluating working definitions, barriers, and implications for animal welfare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it