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Record W4390908422 · doi:10.1186/s12937-023-00895-0

Improving economic access to healthy diets in first nations communities in high-income, colonised countries: a systematic scoping review

2024· article· en· W4390908422 on OpenAlexaboutno aff
Amanda Lee, Lisa‐Maree Herron, Stephan Rainow, Lisa Wells, Ingrid Kenny, Leon Kenny, Imogen Wells, Margaret Kavanagh, Suzanne Bryce, Liza Balmer

Bibliographic record

VenueNutrition Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersUniversity of QueenslandAustralian Government
KeywordsPsychological interventionMedicineSubsidyVoucherGovernment (linguistics)Environmental healthPromotion (chess)Inclusion (mineral)Economic growthBusinessPolitical scienceEconomicsNursingAccountingPolitics

Abstract

fetched live from OpenAlex

BACKGROUND: Affordability of healthy food is a key determinant of the diet-related health of First Nations Peoples. This systematic scoping review was commissioned by the Ngaanyatjarra Pitjantjatjara Yankunytjatjara Women's Council (NPYWC) in Central Australia to identify interventions to improve economic access to healthy food in First Nations communities in selected high-income, colonised countries. METHODS: Eight databases and 22 websites were searched to identify studies of interventions and policies to improve economic access to healthy food in First Nations communities in Australia, Canada, the United States or New Zealand from 1996 to May 2022. Data from full text of articles meeting inclusion criteria were extracted to a spreadsheet. Results were collated by descriptive synthesis. Findings were examined with members of the NPYWC Anangu research team at a co-design workshop. RESULTS: Thirty-five publications met criteria for inclusion, mostly set in Australia (37%) or the US (31%). Interventions (n = 21) were broadly categorised as price discounts on healthy food sold in communities (n = 7); direct subsidies to retail stores, suppliers and producers (n = 2); free healthy food and/or food vouchers provided to community members (n = 7); increased financial support to community members (n = 1); and other government strategies (n = 4). Promising initiatives were: providing a box of food and vouchers for fresh produce; prescriptions for fresh produce; provision/promotion of subsidised healthy meals and snacks in community stores; direct funds transfer for food for children; offering discounted healthy foods from a mobile van; and programs increasing access to traditional foods. Providing subsidies directly to retail stores, suppliers and producers was least effective. Identified enablers of effective programs included community co-design and empowerment; optimal promotion of the program; and targeting a wide range of healthy foods, particularly traditional foods where possible. Common barriers in the least successful programs included inadequate study duration; inadequate subsidies; lack of supporting resources and infrastructure for cooking, food preparation and storage; and imposition of the program on communities. CONCLUSIONS: The review identified 21 initiatives aimed at increasing affordability of healthy foods in First Nations communities, of which six were deemed promising. Five reflected the voices and experiences of members of the NPYWC Anangu research team and will be considered by communities for trial in Central Australia. Findings also highlight potential approaches to improve economic access to healthy foods in First Nations communities in other high-income colonised countries. TRIAL REGISTRATION: PROSPERO CRD42022328326.

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)0.022
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.094
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0180.017
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.421
Teacher spread0.362 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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