Investigating a Train-the-Trainer Model of Supervision and Peer Review for Child Interviewers in Canadian Police Services
Bibliographic record
Abstract
Abstract This project, conducted in one Canadian province, investigated whether a train-the-trainer model of supervision and peer review could improve the interviewing skills of police officers new to interviewing children. At 6 police services, 2 “interview specialists” were chosen by criteria (e.g. having conducted > 30 interviews with children), commitment (minimum 2 years to project), and performance evaluation of a mock and field interview. Specialists received additional training on leading group peer review and individual supervision. They carried out these activities over a 9-month period with 3–4 trainees (new interviewers) per site. Trainees’ interview performance was evaluated with mock and field interviews pre and post intervention. Qualitative interviews about project feasibility were carried out with the specialists at the end of the project, and the results of those interviews comprise the focus of this paper. At the time of the qualitative interviews, only 9 specialists and 5 sites remained in the project. Their interviews revealed that organizational buy-in was critical theme. Other emergent themes were that (different) training is needed for all levels of interviewing experience including interview specialists, that peer review formats are not one-size-fits-all across services, and that fostering a culture of peer review enhances cohesion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".