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Record W3163698439 · doi:10.3138/jvme-2020-0116

Systematic Review of Research on Barriers to Access to Veterinary and Medical Care for Deaf and Hard of Hearing Persons

2021· review· en· W3163698439 on OpenAlexvenueno aff
Allison N. Hinchcliff, Kelly Harrison

Bibliographic record

VenueJournal of Veterinary Medical Education · 2021
Typereview
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careInclusion (mineral)MedicinePopulationVeterinary medicineFamily medicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.344
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.704
GPT teacher head0.685
Teacher spread0.019 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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