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Record W6980825922

Counseling with Deaf Clients: The need for Culturally and Linguistically Sensitive Interventions

2019· article· en· W6980825922 on OpenAlexaff

Bibliographic record

VenueNSUWorks (Nova Southeastern University) · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Toxicity and Pharmacological Properties
Canadian institutionsCanadian Cardiovascular Society
Fundersnot available
KeywordsPsychological interventionSilenceIntervention (counseling)Session (web analytics)Work (physics)American Sign LanguageSign languageDeaf culture
DOInot available

Abstract

fetched live from OpenAlex

Many counselors and therapists who work with culturally Deaf clients have had the unfortunate experience of trying to use what they consider to be an appropriate intervention, only to see it elicit a response of silence and puzzlement from the client. At other times the clinician may become creative and use an intervention that seems to propel the session forward. Why is it that certain types of interventions tend to work better with Deaf clients? Why is it that some interventions seem inappropriate? Are there certain types of interventions that are best suited for use within a visual/gestural mode of communication? The two premises being put forth in this paper are: some interventions are, in fact, better suited for work with Deaf individuals; and, the natural language of Deaf people, namely American Sign Language (ASL)as well as factors pertaining to Deaf culture, should be considered essential criteria for determining which interventions are appropriate. This paper is not meant as a "how to" document but, rather, as an invitation to counselors who work with Deaf clients to examine the extent to which their interventions complement the linguistic and cultural needs of their clients. Three examples of culturally and linguistically sensitive interventions will be examined in this paper and then applied to a case example.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes1
Has abstractyes

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