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Record W1997422773 · doi:10.1300/j010v44n03_01

Do Culturally Sensitive Services for Chinese In-Patients Make a Difference?

2007· article· en· W1997422773 on OpenAlexaffabout
Joseph Ng, Svetlana Popova, Myra Yau, Joanne Sulman

Bibliographic record

VenueSocial Work in Health Care · 2007
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsInterpreterCultural diversitySocial workCulturally appropriateCulturally sensitiveCultural competenceLimited English proficiencyHealth careLanguage barrierMedicineLanguage proficiencySample (material)NursingPsychologyFamily medicineSocial psychologySociologyLinguisticsPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Hospitals in large cities provide services to an increasingly diverse linguistic and cultural clientele. Some strategies adopted to improve services to non-English speaking populations include the use of bilingual social workers, interpreters and printed translation tools. In order to identify gaps in culturally sensitive care in a Canadian teaching hospital, this study surveyed a consecutive sample of 279 Chinese in-patients to determine satisfaction with hospital experience, levels of information about hospital routines and awareness of on-site Chinese cultural services. Results were generally positive. However, satisfaction and information levels were significantly higher among those patients who were aware of culturally appropriate Chinese resources such as social workers, cultural interpreters, and culturally specific reference tools. Of particular interest are the 121 patients (44%) who were less comfortable with English, since awareness of culturally specific resources tended to make a greater difference to this sub-group. These results can potentially help health care providers improve services to patients and families from diverse cultural and linguistic groups.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.438
Teacher spread0.410 · 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 designObservational
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

Citations17
Published2007
Admission routes2
Has abstractyes

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