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Record W2097898280 · doi:10.1067/mpn.2000.108534

Use of Peplau's Interpersonal Relations Model to Counsel People With AIDS

2000· article· en· W2097898280 on OpenAlexaff
Paul-André Gauthier

Bibliographic record

VenueJournal of the American Psychiatric Nurses Association · 2000
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLonelinessPsychologyNursingQualitative researchInterpersonal relationshipInterpersonal communicationHuman immunodeficiency virus (HIV)MedicineSocial psychologyFamily medicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Although faced with serious concerns that need to be addressed, clients are often left alone as they cope with AIDS. Nurses are sometimes unsure of their ability to help persons who have termi nal illnesses. OBJECTIVES: The purposes of this research were to provide an example of the development of a nursing approach by the use of Peplau's interpersonal relations model and to gain a greater understanding of life-and-death issues raised by men and women with HIV and AIDS. STUDY DESIGN: A qualitative analysis of a man with AIDS was completed by use of Peplau's model. RESULTS: A question, such as "What are your concerns regarding your situation or your disease?" can greatly encourage clients facing a terminal illness to discuss their concerns. Nurses can assist clients in discussing their concerns regarding death. Nurses can create trusting relationships with clients and understand various issues facing the clients and the interaction process involved. Greater knowledge and understanding of these issues are gained when looking at three categories of concern: care and dis ease, life and death, and stereotypes and prejudices. CONCLUSION: Nurses can make a qualitative difference in the lives of clients with AIDS when they accom pany these persons on their journey. Nurses can minimize loneliness and provide professional support to clients dealing with HIV and AIDS. (J Am Psycbiatr Nurses Assoc [2000]. 6 119-125.)

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.310
Teacher spread0.298 · 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 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

Citations0
Published2000
Admission routes1
Has abstractyes

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