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Record W4318916514 · doi:10.1177/23333936221148808

Sociocultural Factors Affecting Breastfeeding Practices of Mothers During Natural Disasters: A Critical Ethnography in Rural Pakistan

2023· article· en· W4318916514 on OpenAlexafffund
Shela Akbar Ali Hirani, Solina Richter, Bukola Salami, Helen Vallianatos

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2023
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanUniversity of Regina
FundersCanadian Institutes of Health ResearchInternational Development Research CentreSigma Theta Tau International
KeywordsBreastfeedingSociocultural evolutionEthnographyGeographyPsychologySocioeconomicsNatural disasterSociologyAnthropologyMedicinePediatrics

Abstract

fetched live from OpenAlex

Natural disasters affect the health and well-being of mothers with young children. During natural disasters, this population is at risk of discontinuation of their breastfeeding practices. Pakistan is a middle-income country that is susceptible to natural disasters. This study intended to examine sociocultural factors that shape the breastfeeding experiences and practices of internally displaced mothers in Pakistan. This critical ethnographic study was undertaken in disaster-affected villages of Chitral, Pakistan. Data were collected utilizing multiple methods, including in-depth interviews with 18 internally displaced mothers and field observations. Multiple sociocultural factors were identified as either barriers or facilitators to these mothers' capacities to breastfeed their children. Informal support, formal support, breastfeeding culture, and spiritual practices facilitated displaced mothers to sustain their breastfeeding practices. On the other hand, lack of privacy, cultural beliefs, practices and expectations, covert oppression, and lack of healthcare support served as barriers to the breastfeeding practices of displaced mothers.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.172
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.156
GPT teacher head0.565
Teacher spread0.409 · 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 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

Citations46
Published2023
Admission routes2
Has abstractyes

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