180: Preparing to Interact with the Legal System: It's Child's Play!
Bibliographic record
Abstract
Child maltreatment (CM) is a widespread problem in children and youth. Pediatricians and health professionals involved may be required to interact with child welfare authorities and the court system; arenas in which they may be unfamiliar. Professionals often have little experience in reporting suspected CM and frequently don't feel prepared to provide court testimony. Even for those specializing in CM, stress surrounding court appearances is reported. Exposure to and familiarity with this element of practice could increase comfort and competence. Games are an established and effective teaching method. However, evidence for game-based learning among health professionals is limited. To assess the satisfaction with and perceived learning from a game-based tool designed to assist with preparation for court. A board game with content developed by an expert CM group was created. Game content, in the form of questions and tasks, reflected knowledge and skills required by the court system. Attendees at a session on ‘preparing for court’ at a national Child Maltreatment Symposium played the game for one hour. An anonymous post-session survey was completed to assess participants' satisfaction and perceived learning. Responses to questions on a 5-point Likert scale were coded and analyzed using descriptive statistics. Qualitative comments were analyzed and grouped by emerging themes. Forty-three of the 58 players completed a survey yielding a response rate of 74%. Thirty-four respondents (79%) self-identified as a pediatrician or Child Abuse pediatrician, 9% (n=4) were trainees and 12% (n=5) were allied health practitioners. Over half of the respondents were between the ages of 25–45 (n=22). The mean number of years in practice was 19 (range 2–46). Respondents most often “agreed” that the game was: useful for learning (88%), helped link knowledge to practice (56%) and met their educational needs (68%). The vast majority of respondents “agreed” or “strongly agreed” (70%) they would participate in a similar session in the future. When asked to compare the game experience to prior educational sessions about court, respondents most often “agreed” or “strongly agreed” (81%) that the game held their attention better. Qualitative feedback, in the form of exemplar quotes also supported respondents' satisfaction with the game as a learning tool. These results suggest that game-based learning is an effective and positively accepted method of learning about court. Similar game based interactive sessions may be useful for education in other areas with limited preparation options and opportunities for practical experience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".