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Record W6980108577

Assessing Differences in Household Food Insecurity Vulnerabilities Post-Cyclone Idai in Beira, Mozambique

2024· dissertation· en· W6980108577 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsFood securityThematic analysisFood insecurityScale (ratio)Qualitative propertySustainabilityQualitative researchPoverty
DOInot available

Abstract

fetched live from OpenAlex

Food insecurity is a complex sustainability challenge that is being worsened by frequent extreme weather disasters, especially within low-to-middle-income-countries (LMICs). Mitigating post-disaster food insecurity requires data for targeted interventions. Yet, there is limited research on household-characteristics connections with post-disaster food insecurity in LMICs. This study therefore focused on the aftermath of the 2019 Cyclone Idai disaster in Beira, Mozambique, and examined the differences in household food insecurity vulnerabilities using household and personal food environment characteristics, and adaptations to the disaster. Social-ecological systems (SES) theoretical and disaster management lenses informed the collection of data across household (microsystem), community (mesosystem) and humanitarian institutions (macrosystem) levels, as well as the assessment of household food insecurity vulnerabilities. A mixed-methods sequential explanatory study design was employed. The quantitative study entailed a household survey that collected data from 975 households. However, descriptive, univariate and bivariate statistical analyses were conducted on n=709, which had a complete set of data for the Household Food Insecurity Access Scale (HFIAS) measurement of food insecurity, and the household, personal food environment and adaptation to disaster variables. The follow-up qualitative study entailed the use of interview guides to conduct audio-recorded focus-group discussions with households and community leaders, and key-informant interviews with selected personnel from humanitarian institutions addressing food insecurity. The qualitative data was transcribed verbatim, and thematic content analysis was applied. Both quantitative and qualitative results were triangulated to present the findings. There were statistically significant increases in household food insecurity one month after the cyclone compared to the month before levels (p<0.05), with the median HFIAS score increasing from 14 to 18 post-Cyclone Idai. The presence of multiple vulnerability characteristics such as large household sizes, severe underlying food insecurity and low-income within a household, influenced more severe food insecurity post-Cyclone Idai. Also, the displaced households of the study were isolated from food markets and had pre-existing food accessibility challenges within their personal food environment, which was compounded by the loss of houses post-cyclone. Most adaptations were made during Cyclone Idai response and not preparedness. Adaptations to the disaster that enabled food access included the use of household savings, and food-sourcing facilitated by bridging and linking social capital at the mesosystem and macrosystem levels. Regardless, the facilitation of food-sourcing adaptations was constrained by macrosystem level challenges in targeting vulnerable households for food aid distribution. Additionally, non-reciprocal bonding social capital interactions created food access constraints for households that gave to others. The findings support the mitigation of recurrent, severe post-disaster household food insecurity episodes in Beira, Mozambique. This requires the integration of interventions for household food insecurity, disaster risk reduction and equitable food systems, all underpinned by well-coordinated stakeholder collaborations across all SES levels.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.350
Teacher spread0.238 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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