Food Management Behaviours: In Food-insecure, Lone Mother-led Families
Bibliographic record
Abstract
PURPOSE: Little is known about how food is managed in households where food resources are scarce. In this study, the household food management behaviours utilized by food-insecure, lone mother-led families from Atlantic Canada were characterized, and relationships among these behaviours and diet quality were examined. METHODS: Thematic analysis of 24 in-depth interviews from a larger study of mother-led, low-income families was integrated with sociodemographic characteristics, food-insecurity status, and four weekly 24-hour dietary recalls for all household members to yield a family behaviour score (FBS) as a summative measure of food management behaviours, and a healthy plate score (HPS) as a measure of diet quality. RESULTS: Five distinct food management behaviours were identified: authoritative, healthism, sharing, structured, and planning behaviours. An increase in the FBS was associated with a proportional increase in the HPS. Authoritative, healthism, and planning food management behaviours were the strongest predictors of the HPS for all household members (p<0.05). The structured management behaviour was related to the degree of food insecurity. CONCLUSIONS: The FBS and HPS tools hold promise as a way to identify food-insecure families at risk of low diet quality. The next phase of this research will validate the use of these tools in the practice setting.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".