Pediatric Concussion Health Service Utilization and Follow-Up Care: A Population-Based Epidemiological Study Using Administrative Health Data
Bibliographic record
Abstract
Concussion is a common injury among children and youth, although population-level incidence and trends related to service use are not well described in the literature. In addition, while treatment and management decisions are led by best practices and clinical guidelines, there is a paucity of studies exploring the individual and contextual factors that impact health service utilization following concussion in the pediatric patient. Thus, the objective of this thesis was to address these gaps and better understand how children and youth are interacting with the health care system following concussion in Alberta. In this thesis, 14-years of system-level linked administrative health data and a defined episode of care (EOC) were used to describe trends in health care utilization following pediatric concussion in Alberta. An increased incidence of concussion and other mild head injury diagnoses was observed across the province. In addition, a shift in care from emergency department (ED) to outpatient physician office (PO) settings and a higher use of the ED by some segments of the population was observed. Findings suggest some children and youth are more likely to receive care following a concussion. In addition, follow-up care increased over time, demonstrating accordance with clinical guidelines. However, rates remained low, indicating a lack of application by provider or adherence by patient. Findings indicate that the likelihood of receiving follow-up care in Alberta was influenced by both individual and contextual factors. Factors related to need (perceived and evaluated) were most strongly associated with health care utilization. The index visit occurring in PO had the strongest positive association with follow-up care, followed by a history of concussion-related EOC. At the same time, patient predisposing and enabling factors also affected utilization. Younger children and youth, females, and those from areas of lower socioeconomic status (SES) or residing in certain geographical areas were less likely to receive follow-up care. Findings suggest that to improve service delivery and targeted treatment in line with clinical guidelines for all children and youth, policies that focus on equitable access are needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".