RESIDENTIAL CARE IN ISRAEL: PRINCIPLES FOR CONSTRUCTING A COMPUTERIZED SYSTEM FOR GATHERING DATA, PLANNING INTERVENTIONS, AND EVALUATING OUTCOMES
Bibliographic record
Abstract
The project described here aimed to assist the Residential Placement Unit of the Ministry of Welfare and Social Affairs in developing tools for planning interventions for children in their care, monitoring activities and practices, and assessing outcomes. A major requirement was to ensure that the data produced would be relevant to field workers and support their daily therapeutic work with the children. The tools also facilitate ongoing follow-up on the children’s characteristics, needs, strengths, and prior interventions, including evaluating their effectiveness. This information is organized and can be presented in outputs tailored to the needs of field workers, supervisors, and policymakers. Key principles that guided the project were: collaboration among a multitiered team; involvement of service recipients and care leavers (“experts by experience”); balancing the needs of policymakers, staff and field workers; use of standardized and accepted terminology; reliance on a shared measurement framework; and use of outcome-based thinking to structure the system and its components. The implementation of such a computerized system often raises apprehension or resistance among both managers and staff. To address this, a lengthy and in-depth process of building trust took place, including training sessions that communicated the rationale behind the system’s development and the principles underlying its design, and the establishment of a structured feedback mechanism to assess the staff’s acceptance of the system. The system was successfully assimilated and is in routine use in all the residential care facilities of the Ministry of Welfare. Several factors were identified to explain this success: the commitment of the administration of the Residential Placement Unit to this project; the availability of an existing computerized system upon which to develop the project; and the involvement of the research team in the characterization of the system, training the staff, and refining and modifying the system based on the feedback received.
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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 | 0.127 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".