Child Protective Services and University-Based Partnerships: A Participatory Action-Based Model for Creating and Sharing Knowledge
Bibliographic record
Abstract
This paper presents one model for building and sustaining a research partnership between researchers and professional staff in child protection (CPS) agencies. The Maltreatment and Adolescent Pathways (MAP) study was designed to assess the health and well-being of the population of adolescents involved in the child welfare system of a major urban area. The study involved the collaboration between university based researchers and a range of child welfare staff, from administration to front-line workers. A key factor supporting collaboration was reciprocity with expertise, with CPS practitioner knowledge yielding intervention-relevant study queries and constructs, and researcher knowledge on health content and best practices yielding tailored training opportunities and increased climate for knowledge uptake. The MAP study combined a Participatory Action Research (PAR) model with a traditional, scientific positivist model, including the scientific elements of standardized measures, explicit evaluation of the participatory process, and research impact on the community members. This study: 1) provides information on the process of creating effective researcher-CPC agency partnerships, 2) considers key ethics issues, such as the participant’s reactivity to research of child welfare- involved clients, and 3) examines the implications of implanting a PAR approach in research with Aboriginal CPS agencies, as per the required use of the Canadian Institutes of Health Research (CIHR) Guidelines for Health Research Involving Aboriginal People for future community- university partnerships.
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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 | 0.075 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".