Supporting the “Casa Lar” social educator: a case study of a consultation using intercultural knowledge translation/Apoiando o educador social no Casa Lar: um estudo de caso de uma consulta usando translação de conhecimento intercultural
Bibliographic record
Abstract
Children living in foster care have the right to live in a supportive and caring environment, yet studies show that many foster caregivers find it challenging to adequately meet the child’s individual needs. Organizations providing services to children in foster care are responsible for supporting caregivers in this role. This case study describes an occupational therapy (post-professional master’s student) consultation with a group foster home (Casa Lar), in São Paulo, Brazil, to implement a training and support program for their caregivers. A needs assessment revealed the necessity to implement a comprehensive professional development program, based on organizational values and caregiving competencies. Using the Canadian Practice Process Framework as a guide, a multicultural group of clinician administrators completed a cycle of knowledge translation to identify current knowledge in the area, and adapt it to the local context. Active learning and participant action strategies were included in the training program, with a plan to jointly develop a knowledge translation tool that will enable a change in caregiver practice. We present plans for a formative and summative program evaluation, along with reflections on the enabling nature of the consultation.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".