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Record W6990438051

Discharge Instructions for Spanish-Speaking Patients: A House Staff Perspective

2023· other· en· W6990438051 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2023
Typeother
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Quarter (Canadian coin)Descriptive statisticsHealth carePopulationQuality (philosophy)Hospital dischargeMEDLINEHouse staff
DOInot available

Abstract

fetched live from OpenAlex

\n Background\nUC San Diego Health has a diverse patient population with a large portion of its hospitalized patients speaking Spanish as their primary language. Several measures have been taken to overcome barriers to quality healthcare in this subgroup of patients, including easy access to medical translators and post-discharge follow-up efforts; however, there may be room for further improvement. One barrier that remains is providing written hospital discharge instructions for Spanish-speaking patients in their native language. The purpose of this study was to measure the overall perspective of physicians practicing hospital medicine at UC San Diego regarding our ability to effectively provide discharge instructions to Spanish-speaking patients that maximize positive health outcomes after hospitalization. \n Methods\nA seven-question survey was designed to measure the perspectives of hospital staff, including resident and attending physicians, practicing hospital medicine within the UC San Diego healthcare system. In March 2023, the survey was distributed electronically to all resident physicians enrolled in and select administrative attending physicians involved in the UC San Diego internal medicine residency program. Participants were given a two-week period to complete the survey. All participation was voluntary, and responses were collected anonymously. The responses were subsequently analyzed using descriptive statistics. \n Results\nThirty-five participants completed the survey in its entirety, representing approximately a quarter of the internal medicine residency program. Data analysis revealed that 74% of participants “always” or “often” write discharge instructions for Spanish-speaking patients in English. Furthermore, a majority 91% of participants felt that providing instructions in English to Spanish-speaking patients “always” or “often” represents a barrier to care. 100% of participants indicated that, at a minimum, they would “sometimes” use pre-written translator-approved Spanish phrases if provided, with over half of participants replying that they would “always” use these instructions. Lastly, a majority of participants felt that the use of these phrases in Spanish would improve overall follow-up and medication adherence, as well as reduce readmission rates. \n Conclusions\nConsidering these data, it appears that the current method of providing discharge instructions written in English to Spanish-speaking patients is considered a barrier to adequate healthcare at UC San Diego hospitals by internal medicine house staff. Though unlikely to completely resolve the problem, using translator-approved Spanish phrases in discharge instructions may improve follow-up and medication use after discharge, and reduce readmission rates among Spanish-speaking patients. These data will help support current efforts to provide Spanish discharge instructions for Spanish-speaking patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.038
GPT teacher head0.339
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2023
Admission routes1
Has abstractyes

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