What Gets Measured Gets Counted: Food, Nutrition, and Hydration Non-Compliance in Ontario Long-Term Care Homes and the Role of Proactive Compliance Inspections, 2024
Bibliographic record
Abstract
Food and nutrition services are critical to the health of long-term care home (LTCH) residents, yet little is known about how regulatory inspections detect non-compliance with Food, Nutrition, and Hydration (FNH) standards. We conducted a cross-sectional study of administrative inspection data from all licensed LTCHs in Ontario, Canada. One inspection report was randomly selected per LTCH, yielding a sample of 623 LTCHs. The data were collected for the period spanning 1 January 2024 to 31 December 2024. The primary exposure was use of the FNH inspection protocol, and the outcome was FNH non-compliance, defined as at least one Written Notification or Compliance Order. Statistical analyses included chi-square tests for categorical variables and independent samples t-tests (including Welch’s t-tests where appropriate) for continuous variables, with effect sizes (Φ, Cramer’s V, Cohen’s d) reported to complement p-values. This study did not require research ethics review under Western University policy, consistent with Canada’s Tri-Council Policy Statement (TCPS 2, Article 2.2) regarding use of publicly available data. FNH non-compliance was identified in 12.2% (n = 76) of all LTCHs, and in 43.7% of those using the FNH protocol. Use of the FNH protocol was associated with a higher likelihood of detecting FNH non-compliance compared with other inspection protocols (p < 0.001, Φ = 0.55). LTCH ownership and inspection type were also associated with detection patterns. This exploratory study provides the first province-wide analysis of FNH non-compliance in Ontario LTCHs. Findings suggest that inspection protocols influence detection of FNH issues, underscoring the need for further comparative and qualitative research to understand the organizational factors underlying non-compliance.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".