Risk factors for, metrics of, and consequences of access to veterinary care for companion animals: A scoping review
Bibliographic record
Abstract
BACKGROUND: Barriers to accessing veterinary care can be challenging for companion-animal caregivers and may lead to preventable health conditions or even death of pets. OBJECTIVES: We conducted a scoping review to: 1) catalog the definitions of access to veterinary care (A2VC) used by researchers, 2) identify risk factors for and consequences of A2VC, and 3) map the risk factors onto dimensions of access to care (affordability, availability, accessibility, accommodation, acceptability). ELIGIBILITY CRITERIA: Primary research on companion animals not involved in commercial enterprises (e.g., horse racing) examining consequences of and/or risk factors for A2VC for which the full text was available in English. SOURCES OF EVIDENCE: PubMed (1996-6 July 2023) and CAB Abstracts (1973-13 July 2023, Web of ScienceTM) were searched. Additionally, a topic expert (KM) identified relevant references. Two reviewers independently screened titles/abstracts and full texts of potentially relevant references. Forward and backward citation searches were also conducted on all eligible studies using Citation Chaser. CHARTING METHODS: Risk factors were categorized and mapped to the five dimensions of access to care. An evidence gap map was created using the risk factor studies. RESULTS: Fifty-one references describing fifty-two relevant studies were included. Forty-one studied risk factors associated with A2VC, and twelve studied consequences of A2VC. (One study examined both risk factors and consequences.) The majority of risk factors examined were demographic. The majority of outcomes measured were pet-centric. No relevant studies focused on pet horses, representing a gap in the literature. CONCLUSIONS: Consensus needs to be reached on how A2VC is defined and measured to help reduce research wastage and strengthen impact of future studies on improving A2VC. Future studies of risk factors for A2VC should focus on creating a risk-mapping framework specific for A2VC, distinguishing factors that are susceptible to change and those which are not.
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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 | 0.020 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".