A Retrospective Evaluation of A Paediatric Consult Clinic Serving Uninsured Children in Canada
Bibliographic record
Abstract
Abstract BACKGROUND: Children new to Canada may not have health insurance which limits access to health care and increases their vulnerability to poorer health outcomes. As part of a resident advocacy initiative, a monthly pediatric consult clinic was created within a community health clinic to serve uninsured children living in a suburb of a major Canadian city. At the time of its inception in 2009, 35% of patients seen in the health clinic did not have provincial health insurance and an additional 35-40% did not receive Interim Federal Health (IFH) that they were otherwise entitled to (Denburg et al., 2010). OBJECTIVES: To describe the demographics and health issues of uninsured immigrant children referred to a pediatric consult clinic. DESIGN/METHODS: This study is a retrospective chart review characterizing the patients seen from 2009 – 2013. We collected demographic information, presenting symptoms, diagnoses and clinical outcomes. After REB approval, 180 patients were identified and 159 charts were found. Data was extracted from each chart and coded into a database. Of these files, 38 were removed because of duplicate entries or if they were not seen in the clinic. A total of 121 charts were included in the final analysis. An audit was performed by a blinded reviewer to identify any discrepancies in transcription prior to descriptive analysis. RESULTS: Over half (52.1%) of patients seen in our clinic were landed immigrants or Canadians, while 19.8% had refugee status. 49.3% of patients were in a 3-month waiting period for provincial health insurance, while 12.6% had IFH, 11.6% had OHIP and 21.5% had no insurance at all. Only 34% spoke English as their primary language. The top three presenting complaints were developmental, dermatologic and respiratory. The most common final diagnoses were psychiatric and behavioural, dermato-logic and nutritional. The majority of patients required counselling (67%), with 14% prescribed a short medication course and 20% requiring ongoing medication. CONCLUSION: Almost 70% of patients seen had no insurance at the time of their visit, with most in the mandatory 3-month wait for provincial health coverage. Our study suggests that children still need access to health care during this period. The most common diagnoses related to mental health and development suggesting a high prevalence in immigrants and supporting the need to screen for these issues when caring for children new to Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".