The essential triad of the nurse-patient-interpreter relationship with non-English-speaking patients in psychiatric mental health nursing practice
Bibliographic record
Abstract
Non-English speaking (NES) immigrant residents interact with the United States (U.S.). health care system in all settings, inpatient, outpatient and emergency departments. Medical interpreters occupy a central role in mental health care by bridging the communication gap between the patient and the Psychiatric Mental Health Registered Nurse (PMHRN) and the Advanced Practice Registered Nurse (APRN) providers. The authors’ interest in the nature and effects of the presence of the interpreter on the nurse-patient therapeutic relationship began during their clinical experiences in two mental health outpatient settings. The purpose of this article is to describe a clinical occurrence faced by psychiatric mental health nurses in routine practice. It reviews scant literature on changes in dynamics that occur in the provider-patient relationship when an interpreter is present. Secondarily, the authors present interventions for working within the triad partnership of the nurse-patient- interpreter through the lens of Hildegard Peplau’s Interpersonal Relations Theory. The intended outcome of this review is to describe specific interventions for nurses working with interpreters to ease the patient’s mental distress and assist their transition to a higher level of mental health wellness in a new country. Application of the Peplau theory can influence the interpreter's presence in establishing and maintaining the provider-patient therapeutic relationship, as it applies to nursing. The interpreter is essential to interventions for the NES person and is facilitated by the development of a meaningful and productive relationship within the Nurse-Patient-Interpreter triad. This process is critical in reducing the patient’s distress from entry to settlement in the U.S.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".