181: Identifying Unrecognized Needs: A Collaborative Model for the Assessment of High Risk Children Involved with the Child Welfare System
Bibliographic record
Abstract
An estimated 2% of children in Canada suffer mal-treatment. These children have higher rates of medical, developmental and behavioural problems, however their needs may be poorly understood and gaps in service are often identified. In September 2013, the regional pediatric hospital child maltreatment program and the local child welfare agency collaborated to open an outpatient clinic as a pilot project. The goals of the clinic are to provide early, comprehensive and expert identification and intervention in cases of abuse and neglect, as well as to screen for related medical, developmental and behavioural problems. The objectives of this study are to describe the patient population and evaluate the clinic's overall functioning, acceptability and ability to meet the above goals. A logic model delineated the program theory and set out a framework for evaluation. Program function goals included clinical, communication and system issues. Data was collected via chart review and a clinic satisfaction survey for caregivers and child welfare workers. The data was collated and stored in REDCap. Institutional ethics review board approval was granted. Descriptive statistics are reported for the first 12 months. In the first year, 87 children were seen through 134 patient encounters. Most children were referred by child welfare workers (70%). The most common reasons for referral were physical abuse (54%), neglect (29%), trauma and behavior issues (both 14%). New diagnoses were made for 79% children with an average of 2.8 (range 1–8) per patient. Laboratory investigations were required for 58% children, diagnostic imaging for 25% and referrals to other specialized services for 56%. The clinic satisfaction survey was completed by 43 respondents. The overall experience in the clinic was rated as excellent by 86%. Only 20.5% respondents indicated that they had an excellent understanding of the child's health needs before the clinic visit but this increased to 61.9% after the visit. All respondents felt that the new information shared was relevant for the child's health. Most respondents (88.4%) agreed that the new information was relevant for child welfare's involvement with the child. This model of care for high risk children involved with the child welfare system is innovative in our region in that the expert assessments and recommendations are created, communicated and implemented collaboratively by medical and child welfare staff. Preliminary data demonstrates that this population has a high rate of unrecognized health needs and that this model has high ratings of satisfaction by the users.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".