Digital Photography-Assisted Weighing to Conduct Plate Waste Audits in Elementary Schools: Research Assistants’ Perspectives and Experiences
Bibliographic record
Abstract
The Good Food for Learning Population Health Intervention Research Project piloted a universal school lunch program in select Saskatoon elementary schools. Digital photography-assisted weighing (DPAW) was used to conduct plate waste audits to determine student food consumption. DPAW is a novel method, thought to be too labour-intensive to be used in schools. The purpose of the study was to determine the practicalities of using DPAW plate waste audits in elementary schools from research assistants' (RAs') viewpoint. Semi-structured, 30-45-minute virtual interviews were conducted with former RAs and research supervisors involved in data collection during the 2021 and 2023 phases of the Good Food for Learning Project. Ultimately, 11 of 16 prospective participants were interviewed. A hybrid approach of deductive and inductive data coding was used for thematic analysis. We found that although most of the RAs wanted to improve in-person training and coordination with school staff, most reported that they would recommend this method to other researchers carrying out studies under similar settings. This study shows that RAs support the feasibility of DPAW plate waste audits in elementary schools, though some challenges need to be addressed to improve implementation.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".