Family-Rated Pediatric Health Status Is Associated With Unplanned Health Services Use
Bibliographic record
Abstract
OBJECTIVE: Self-rated health is a common self-reported health measure associated with morbidity, mortality, and health care use. The objective was to investigate the association of family-rated health status (FRH) in pediatric care with administrative indicators, patient and respondent features, and unplanned health services use. PATIENTS AND METHODS: Data were taken from Child-Hospital Consumer Assessment of Healthcare Providers and Systems surveys collected between 2015 and 2019 in Alberta, Canada and linked with administrative health records. Three analyses were performed: correlation to assess association between administrative indicators of health status and FRH, logistic regression to assess respondent and patient characteristics associated with FRH, and automated logistic regression to assess the association between FRH and unplanned health services use within 90 days of discharge. RESULTS: A total of 6236 linked surveys were analyzed. FRH had small but significant associations with administrative indicators. Models of FRH had better fit with patient and respondent features. Respondent relationship to child, child age, previous hospitalizations, and number of comorbidities were significantly associated with ratings of FRH. Automated models of unplanned services use included FRH as a feature, and poor ratings of health were associated with increased odds of emergency department visits (adjusted odds ratio: 2.15, 95% confidence interval: 1.62-2.85) and readmission (adjusted odds ratio: 2.48, 95% confidence interval: 1.62-2.85). CONCLUSION: FRH is a simple, single-item global rating of health for pediatric populations that provides accessible and useful information about pediatric health care needs. The results of this article serve as a reminder that family members are valuable sources of information that can improve care and potentially prevent unplanned health services use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".