Food insecurity is associated with poor social capital, perceived health, and perceived diet among adult food bank users in and around the lower mainland of British Columbia, Canada
Bibliographic record
Abstract
Adult clients from 4 food banks in British Columbia (Surrey, Richmond, TriCities, Nanaimo) were surveyed for differences between food security situation among adults (FSSAA), social capital (SC), perceived health (HLTH), and perceived diet (DIET). Of 1,064 invited, 528 (49.6% response rate) completed the study. For FSSAA, only 5.5% were food secure; 26.3% and 68.2% were food insecure (moderate) and food insecure (severe), respectively. 42.2% had high SC, while 57.8% had low. 34.9% considered their HLTH to be poor/fair, while 65.1% considered it to be good/very good/excellent. 50.3% considered their DIET to be poor/fair, while 49.7% considered it to be good/very good/excellent. FSSAA [Kruskal‐Wallis (K‐W), p=.046], SC (K‐W, p=.003), and DIET (K‐W, p=.030) significantly differed by food bank, while HLTH did not (K‐W.341). Considering all participants, FSSAA was significantly related (Kendall's Tau b) to SC (−0.141, p<.001), HLTH (−0.196, p<.001), and DIET (−0.290, p<.001). This study confirms that food bank users are food insecure and have poor SC, HLTH, and DIET. It also underscores the negative relationship of food insecurity to those constructs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".