MétaCan
Menu
Back to cohort
Record W2895561513 · doi:10.1007/s11266-018-0039-2

Examining Patterns of Food Bank Use Over Twenty-Five Years in Vancouver, Canada

2018· article· en· W2895561513 on OpenAlexafffundabout
Jennifer Black, Darlene Seto

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchScheme for Promotion of Academic and Research CollaborationMiljøstyrelsenJ.W. McConnell Family Foundation
KeywordsDuration (music)BusinessFood serviceService (business)Cluster (spacecraft)Environmental healthMedicineGeographyDemographyGerontologyMarketingSociology

Abstract

fetched live from OpenAlex

Food banks have grown substantially in Canada since the 1980s but little is known about patterns or predictors of engagement including frequency or duration of service use. This study examined food bank program data from a large food bank organization in Vancouver, Canada, finding that between January 1992 and June 2017, at least 116,963 individuals made over 2 million food bank visits. The majority of members were engaged for a short time and came for relatively few visits, but 9% of members engaged in longer-term episodic or ongoing usage over several years, accounting for 65% of all visits. Results from cluster and regression analyses found that documented health and mobility challenges, larger household size, primary income source, and older age were predictors of higher frequency and duration of service usage. Findings add to growing critical examinations of the "emergency food system" highlighting the need for better understanding of the broader social policies influencing food bank use.

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 score0.022
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.054
GPT teacher head0.351
Teacher spread0.297 · 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

Citations35
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueVOLUNTAS International Journal of Voluntary and Nonprofit OrganizationsSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207