91 Health care needs and missed care among youth in care in British Columbia: A population study
Bibliographic record
Abstract
Abstract Primary Subject area Adolescent Medicine Background Youth in care (YIC), including those living in foster care, kinship care, group homes, and youth agreements, are a vulnerable population with many risk factors leading to a high prevalence of mental and physical health needs. YIC are recommended to have more frequent health care encounters than the general adolescent population, though it is unknown how Canadian YIC perceive whether their health care needs are sufficiently met. Objectives To assess YIC's perception of their health care needs and frequency of missed care, defined as not having received needed health care. Design/Methods A representative sample of 38,015 students in Grades 7 to 12 across British Columbia was surveyed in the 2018 BC Adolescent Health Survey (McCreary Centre Society). Questionnaire items on demographics, mental and physical health, and health care access in the past year were examined. Frequencies and cross-tabulations were performed using IBM SPSS® Complex Samples module software. Results In the past year, 1.9% of respondents reported living in government care. YIC had a mean age of 14.76 years and were 50.9% female. YIC reported worse mental health (46.5% vs. 27.6% poor/fair rating, p < 0.01) and physical health (36.4% vs. 19.1% poor/fair rating, p < 0.01) compared to non-YIC, with female and non-binary YIC most severely impacted. YIC were less likely to report not needing health care (15.6% vs. 21.3%, p < 0.01) and more likely to report missed care (11.2% vs. 3.1%, p < 0.01) compared to non-YIC. Although the rate of any health care usage was not significantly different between the groups, nearly one-quarter (23.7%) of YIC accessed health care at 3 or more locations, compared to only 16.4% of non-YIC (p < 0.01), with YIC accessing counsellors/psychologists and youth clinics more frequently. YIC reported more missed mental health care (32.9% vs. 18.4%, p < 0.01) and physical health care (21.6% vs. 7.8%, p < 0.01) than non-YIC, with female YIC reporting more missed care than male YIC. Non-binary YIC also reported more missed mental health care than male YIC. YIC were more likely than non-YIC to have missed mental health care due to reasons such as prior negative experiences and lack of transportation. Conclusion YIC reported worse mental and physical health and greater frequencies of missed care compared to non-YIC, especially female and non-binary YIC. Further attention is needed in addressing systemic and individual barriers to health care in this vulnerable population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".