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Record W2006563167 · doi:10.1177/1043659607301301

Nursing Care of AIDS Patients in Uganda

2007· article· en· W2006563167 on OpenAlexaff
Bonnie Fournier, Walter Kipp, Judy Mill, Mariam Walusimbi

Bibliographic record

VenueJournal of Transcultural Nursing · 2007
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFocus groupNursingMedicineReferralGovernment (linguistics)Participatory action researchPovertyPhotovoiceFamily medicinePolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

This article reports the findings from a participatory action research study concerning the experience of Ugandan nurses caring for individuals with HIV illness. Six key informants from government and non-governmental organizations were interviewed using a semistructured format. Six nurses from a large national referral hospital in Kampala, Uganda, participated in 10 focus group meetings during a period of 11 months. In-depth interviews, focus groups, and photovoice were used to collect the data. Findings indicate that nurses faced many challenges in their daily care, including poverty, insufficient resources, fear of contagion, and lack of ongoing education. Nurses experienced moral distress due to the many challenges they faced during the care of their patients. Moral distress may lead nurses to quit their jobs, which would exacerbate the acute shortage of nurses in Uganda. This study provides important knowledge for guiding clinical practice and nursing education in resource-constrained countries like Uganda.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.311
Teacher spread0.301 · 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 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

Citations42
Published2007
Admission routes1
Has abstractyes

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