Assessment of Institutions
Bibliographic record
Abstract
This chapter traces the development of institutions in the United States, Canada, and Great Britain, as well as the need for inspections and reform. Some of this reform was driven by the deinstitutionalization movement with an emphasis on community-based care that embraced the concepts of normalization and least restrictive environment. Current frameworks for assessing institutional effectiveness are compared. Highlighting the shift to quality assurance, evidence-based assessment approaches, and their limitations are reviewed as well as their implications at the macro, mezzo, and micro levels of practice. Recognizing that institutions and programs must be continually evolving, the key elements of continuous quality improvement is discussed. Promising practices, such as institutions that are data-driven, using outcomes to generate improvements and innovations coupled with the notion of consumer guided change, are presented.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".