Systematic Review of Research on Barriers to Access to Veterinary and Medical Care for Deaf and Hard of Hearing Persons
Bibliographic record
Abstract
The Deaf and hard of hearing (DHH) population suffers disproportionately from barriers to health care access. Progress has been made toward improving access to medical care in the human health field; however, the veterinary field has not yet implemented similar standards. More research is needed to improve access to veterinary care for disabled individuals. This systematic review aimed to evaluate all primary research articles pertaining to medical and veterinary health care access for DHH adults in the United States. Its purpose was to assess gaps in knowledge regarding DHH persons' access to veterinary care. The review includes 39 articles related to DHH access to medical care and 6 articles related to general access to veterinary care. The authors found no articles related specifically to DHH access to veterinary care nor any articles on disability accessibility to veterinary care that met the inclusion criteria. Results outline significant barriers to DHH persons' access to health care, unique needs specific for this population of patients, and recommendations to improve access to medical care for individuals who identify as DHH. The results also suggest that further research is needed to investigate barriers to veterinary care experienced by DHH pet owners, the unique needs of this population of pet owners, and how the field of veterinary medicine can better accommodate those needs.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".