A Critical Look at Food Security in Social Work: Applying the Socio-ecological Lens
Bibliographic record
Abstract
One in six children under the age of 18 in Canada lives in a food insecure household. This is deeply concerning as the presence of food insecurity can disrupt developmental trajectories potentially impacting the lifespan of a child. However, when compared to other social problems, food security takes a backseat. Twenty-four years ago, a call to action was issued to social workers to make food security a priority within their practice. The literature demonstrates a slow but encouraging rise in the number of social workers heeding that call. This paper provides a critical analysis of twenty-one articles investigating social work and food security interventions. The articles were published in peer-reviewed, academic journals between 1993 and 2016. The socio-ecological model was used to guide the review of the articles to help extrapolate how social workers can address food security at the microsystem, mesosystem, exosystem, macrosystem, and chronosystem level. Forty-three interventions were identified. Most of the interventions considered the exosystem and macrosystem level of practice, which highlighted the importance of building strong communities and implementing policies for “food justice”. The results also indicate that front-line social workers are well suited for food security interventions, but comprehensive research on how microsystem, mesosystem and chronosystem level strategies are best executed would help bring them to fruition. Furthermore, implementing food security into social work curriculum and becoming food conscious themselves was highly recommended.
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 | 0.035 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".