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Cognitive coping intervention for acutely ill HIV‐positive men

2005· article· en· W2026001923 on OpenAlexafffund
José Côté, Carolyn Pepler

Bibliographic record

VenueJournal of Clinical Nursing · 2005
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersMcGill University
KeywordsCoping (psychology)Human immunodeficiency virus (HIV)CognitionClinical psychologyPsychologyMedicineIntervention (counseling)NursingPsychotherapistPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

AIM: A nursing intervention was designed that is intended to develop, maintain and strengthen the patient's cognitive coping skills to regulate emotional response to severe physical symptoms. It was tested in a randomized trial reported elsewhere and this article describes the intervention and discusses its application. BACKGROUND: Research shows that psychological distress and depressive symptoms are likely to increase with the development of human immunodeficiency virus (HIV)-related physical symptoms. Nurses who work with HIV-positive individuals have an opportunity to influence their patients' health experiences substantially. CLINICAL APPLICATION: The intervention was carried out with hospitalized HIV patients who had to cope with an acute period of stress due to an exacerbation of HIV-related symptoms. The intervention was administered on three consecutive days, in 20-30-minute sessions. The features of the intervention in relation to two examples of patient profiles are described: patients needing to develop their cognitive skills to regulate severe emotional distress and patients with effective cognitive coping skills. RELEVANCE TO CLINICAL PRACTICE: The intervention empowers nurses by offering an effective approach to deal with individuals who are facing severe illness. It has also the potential to empower patients both to take advantage of their own personal resources and to take control over their situation.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.089
GPT teacher head0.520
Teacher spread0.431 · 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 designOther design
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

Citations8
Published2005
Admission routes2
Has abstractyes

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