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Record W2065270144 · doi:10.1155/2014/160363

Exploring Low-Income Families’ Financial Barriers to Food Allergy Management and Treatment

2014· article· en· W2065270144 on OpenAlexafffund
Leia Minaker, Susan J. Elliott, Ann E. Clarke

Bibliographic record

VenueJournal of Allergy · 2014
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcGill UniversityUniversity of Waterloo
FundersAllerGen
KeywordsPreparednessEnvironmental healthFood allergyIndirect costsMedicineAllergyPovertyAnaphylaxisBusinessFamily medicineEconomicsEconomic growthImmunology

Abstract

fetched live from OpenAlex

Objectives. Low-income families may face financial barriers to management and treatment of chronic illnesses. No studies have explored how low-income individuals and families with anaphylactic food allergies cope with financial barriers to anaphylaxis management and/or treatment. This study explores qualitatively assessed direct, indirect, and intangible costs of anaphylaxis management and treatment faced by low-income families. Methods. In-depth, semistructured interviews with 23 participants were conducted to gain insight into income-related barriers to managing and treating anaphylactic food allergies. Results. Perceived direct costs included the cost of allergen-free foods and allergy medication and costs incurred as a result of misinformation about social support programs. Perceived indirect costs included those associated with lack of continuity of health care. Perceived intangible costs included the stress related to the difficulty of obtaining allergen-free foods at the food bank and feeling unsafe at discount grocery stores. These perceived costs represented barriers that were perceived as especially salient for the working poor, immigrants, youth living in poverty, and food bank users. Discussion. Low-income families report significant financial barriers to food allergy management and anaphylaxis preparedness. Clinicians, advocacy groups, and EAI manufacturers all have a role to play in ensuring equitable access to medication for low-income individuals with allergies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.267
Teacher spread0.226 · 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 designOther design
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

Citations29
Published2014
Admission routes2
Has abstractyes

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