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Record W4310939250 · doi:10.1002/nop2.1499

Mothers' perspectives of physical and psychological issues associated with caring for Ghanaian children living with tuberculosis: A qualitative study

2022· article· en· W4310939250 on OpenAlexaff
Eric Tornu, Gladys Dzansi, Donna M. Wilson, Solina Richter, Lydia Aziato

Bibliographic record

VenueNursing Open · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsLonelinessThematic analysisQualitative researchSadnessTuberculosisPsychologyMedicineClinical psychologyPsychiatryAnger

Abstract

fetched live from OpenAlex

AIM: The aim of the study was to explore and describe the mothers' perspectives on issues associated with caring for Ghanaian children aged 0-14 years living with tuberculosis. DESIGN: Exploratory descriptive qualitative design. METHODS: Semi-structured individual interviews were conducted face to face with 15 purposively sampled mothers from two health facilities in the Greater Accra area. Transcribed data were analysed using computer-enhanced thematic analysis. RESULTS: Findings were grouped into (1) physical burden on the mothers and (2) psychological burden on mothers. The eight subthemes indicate that mothers of children living with tuberculosis experienced tiredness, sleeplessness, body pains, weight loss and sicknesses as they cared for their children. In addition to worrying, mothers experienced stigma and negative emotions such as sadness, guilt, fear and loneliness. CONCLUSION: Mothers of children diagnosed with tuberculosis in Ghana experience physical and psychological health problems related to their caregiving roles. Nurses and other healthcare providers should identify specific problems and offer broad-based emotional and other support for these women.

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)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.450
Teacher spread0.385 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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