A Proposed Framework for an Integrated Process of Improving Quality of Life
Bibliographic record
Abstract
Abstract The need for quality of life, both as a concept and as a measure, to be applied to policy and practice has been noted in the disability literature for several years. In 2012, Schalock and Verdugo introduced a conceptual model to help service organizations evaluate if congruence exists among their systems, policies, and practices and, if misalignments exist, to make changes through policy and systems change. Their model focuses on two levels, system‐level processes and organization‐level practices, at three consecutive stages of use: inputs, throughputs, and outputs. In this article, the authors extend the work of Schalock and Verdugo by adding a third level of application, individual‐ and family‐level living, and propose the inclusion of outcomes as a fourth stage of use representing a consequence of outputs. We recognize the dynamic interaction among all components of the conceptual framework and, like Schalock and Verdugo, argue for alignment both vertically (system, organization, and individual and family living levels) and horizontally (inputs, throughputs, outputs, and outcomes) within our revised conceptual framework. Based on this, the authors propose that quality of life outcomes (the ongoing effects of outputs) should be an ultimate focus of service organizations and policy development if quality of life is to be enhanced for individuals with intellectual and developmental disabilities and their families.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".